import { SENSITIVE_STRING } from "@aws-sdk/smithy-client" ; import { ActionSource , ActionStatus , AdditionalInferenceSpecificationDefinition , AlgorithmSpecification , AlgorithmStatus , AlgorithmStatusDetails , AlgorithmValidationSpecification , AnnotationConsolidationConfig , AppNetworkAccessType , AppSecurityGroupManagement , AppSpecification , AppStatus , AppType , ArtifactSource , AsyncInferenceConfig , AthenaDatasetDefinition , AuthMode , AutoMLCandidate , AutoMLChannel , AutoMLJobArtifacts , AutoMLJobCompletionCriteria , AutoMLJobConfig , AutoMLJobObjective , AutoMLJobSecondaryStatus , AutoMLJobStatus , AutoMLOutputDataConfig , AutoMLPartialFailureReason , BatchStrategy , Bias , CaptureStatus , Channel , CheckpointConfig , CognitoConfig , CognitoMemberDefinition , CollectionConfiguration , CompilationJobStatus , ContainerDefinition , ContentClassifier , ContextSource , DataCaptureConfig , DataQualityAppSpecification , DataQualityBaselineConfig , DataQualityJobInput , DeploymentConfig , DomainSettings , EdgeOutputConfig , EdgePresetDeploymentType , EndpointInput , EnvironmentParameterRanges , FeatureDefinition , FlowDefinitionOutputConfig , GitConfig , HumanLoopActivationConfig , HumanLoopConfig , HumanLoopRequestSource , HyperParameterTrainingJobDefinition , HyperParameterTuningJobConfig , HyperParameterTuningJobObjectiveType , HyperParameterTuningJobWarmStartConfig , InferenceSpecification , InputConfig , KernelGatewayImageConfig , MetadataProperties , MetricsSource , ModelApprovalStatus , ModelDeployConfig , MonitoringConstraintsResource , MonitoringNetworkConfig , MonitoringOutputConfig , MonitoringResources , MonitoringStatisticsResource , MonitoringStoppingCondition , NeoVpcConfig , ObjectiveStatus , OfflineStoreConfig , OnlineStoreConfig , OutputConfig , OutputDataConfig , ProblemType , ProcessingInstanceType , ProcessingS3DataDistributionType , ProcessingS3InputMode , ProcessingS3UploadMode , ProductionVariant , ProductionVariantAcceleratorType , ProductionVariantInstanceType , ProductionVariantServerlessConfig , PublicWorkforceTaskPrice , ResourceConfig , ResourceSpec , RetryStrategy , StoppingCondition , Tag , TrainingSpecification , TransformInput , TransformJobDefinition , TransformOutput , TransformResources , UserContext , UserSettings , VpcConfig , } from "./models_0" ; /**
 * <p>The endpoint configuration for the load test.</p>
 */ export interface EndpointInputConfiguration { /**
     * <p>The instance types to use for the load test.</p>
     */ InstanceType : ProductionVariantInstanceType | string | undefined ; /**
     * <p>The inference specification name in the model package version.</p>
     */ InferenceSpecificationName ? : string ; /**
     * <p> The parameter you want to benchmark against.</p>
     */ EnvironmentParameterRanges ? : EnvironmentParameterRanges ; } export namespace EndpointInputConfiguration { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Specifies the maximum number of jobs that can run in parallel
 *     and the maximum number of jobs that can run.</p>
 */ export interface RecommendationJobResourceLimit { /**
     * <p>Defines the maximum number of load tests.</p>
     */ MaxNumberOfTests ? : number ; /**
     * <p>Defines the maximum number of parallel load tests.</p>
     */ MaxParallelOfTests ? : number ; } export namespace RecommendationJobResourceLimit { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Defines the traffic pattern.</p>
 */ export interface Phase { /**
     * <p>Specifies how many concurrent users to start with.</p>
     */ InitialNumberOfUsers ? : number ; /**
     * <p>Specified how many new users to spawn in a minute.</p>
     */ SpawnRate ? : number ; /**
     * <p>Specifies how long traffic phase should be.</p>
     */ DurationInSeconds ? : number ; } export namespace Phase { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum TrafficType { PHASES = "PHASES" } /**
 * <p>Defines the traffic pattern of the load test.</p>
 */ export interface TrafficPattern { /**
     * <p>Defines the traffic patterns.</p>
     */ TrafficType ? : TrafficType | string ; /**
     * <p>Defines the phases traffic specification.</p>
     */ Phases ? : Phase [ ] ; } export namespace TrafficPattern { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The input configuration of the recommendation job.</p>
 */ export interface RecommendationJobInputConfig { /**
     * <p>The Amazon Resource Name (ARN) of a versioned model package.</p>
     */ ModelPackageVersionArn : string | undefined ; /**
     * <p>Specifies the maximum duration of the job, in seconds.></p>
     */ JobDurationInSeconds ? : number ; /**
     * <p>Specifies the traffic pattern of the job.</p>
     */ TrafficPattern ? : TrafficPattern ; /**
     * <p>Defines the resource limit of the job.</p>
     */ ResourceLimit ? : RecommendationJobResourceLimit ; /**
     * <p>Specifies the endpoint configuration to use for a job.</p>
     */ EndpointConfigurations ? : EndpointInputConfiguration [ ] ; } export namespace RecommendationJobInputConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum RecommendationJobType { ADVANCED = "Advanced" , DEFAULT = "Default" } /**
 * <p>The model latency threshold.</p>
 */ export interface ModelLatencyThreshold { /**
     * <p>The model latency percentile threshold.</p>
     */ Percentile ? : string ; /**
     * <p>The model latency percentile value in milliseconds.</p>
     */ ValueInMilliseconds ? : number ; } export namespace ModelLatencyThreshold { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Specifies conditions for stopping a job. When a job reaches a
 *            stopping condition limit, SageMaker ends the job.</p>
 */ export interface RecommendationJobStoppingConditions { /**
     * <p>The maximum number of requests per minute expected for the endpoint.</p>
     */ MaxInvocations ? : number ; /**
     * <p>The interval of time taken by a model to respond as viewed from SageMaker.
     *           The interval includes the local communication time taken to send the request
     *           and to fetch the response from the container of a model and the time taken to
     *           complete the inference in the container.</p>
     */ ModelLatencyThresholds ? : ModelLatencyThreshold [ ] ; } export namespace RecommendationJobStoppingConditions { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateInferenceRecommendationsJobRequest { /**
     * <p>A name for the recommendation job. The name must be unique within
     *            the Amazon Web Services Region and within your Amazon Web Services account.</p>
     */ JobName : string | undefined ; /**
     * <p>Defines the type of recommendation job. Specify <code>Default</code> to initiate an instance
     *            recommendation and <code>Advanced</code> to initiate a load test. If left unspecified,
     *            Amazon SageMaker Inference Recommender will run an instance recommendation (<code>DEFAULT</code>) job.</p>
     */ JobType : RecommendationJobType | string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker
     *     to perform tasks on your behalf.</p>
     */ RoleArn : string | undefined ; /**
     * <p>Provides information about the versioned model package Amazon Resource Name (ARN),
     *     the traffic pattern, and endpoint configurations.</p>
     */ InputConfig : RecommendationJobInputConfig | undefined ; /**
     * <p>Description of the recommendation job.</p>
     */ JobDescription ? : string ; /**
     * <p>A set of conditions for stopping a recommendation job.  If any of
     *           the conditions are met, the job is automatically stopped.</p>
     */ StoppingConditions ? : RecommendationJobStoppingConditions ; /**
     * <p>The metadata that you apply to Amazon Web Services resources to help you
     *            categorize and organize them. Each tag consists of a key and a value, both of
     *            which you define. For more information, see
     *            <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services Resources</a>
     *            in the Amazon Web Services General Reference.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateInferenceRecommendationsJobRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateInferenceRecommendationsJobResponse { /**
     * <p>The Amazon Resource Name (ARN) of the recommendation job.</p>
     */ JobArn : string | undefined ; } export namespace CreateInferenceRecommendationsJobResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Provided configuration information for the worker UI for a labeling job. Provide
 *             either <code>HumanTaskUiArn</code> or <code>UiTemplateS3Uri</code>.</p>
 *         <p>For named entity recognition, 3D point cloud and video frame labeling jobs, use
 *                 <code>HumanTaskUiArn</code>.</p>
 *         <p>For all other Ground Truth built-in task types and custom task types, use
 *                 <code>UiTemplateS3Uri</code> to specify the location of a worker task template in
 *             Amazon S3.</p>
 */ export interface UiConfig { /**
     * <p>The Amazon S3 bucket location of the UI template, or worker task template. This is the
     *             template used to render the worker UI and tools for labeling job tasks. For more
     *             information about the contents of a UI template, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates-step2.html"> Creating Your Custom
     *                 Labeling Task Template</a>.</p>
     */ UiTemplateS3Uri ? : string ; /**
     * <p>The ARN of the worker task template used to render the worker UI and tools for
     *             labeling job tasks.</p>
     *         <p>Use this parameter when you are creating a labeling job for named entity recognition,
     *             3D point cloud and video frame labeling jobs. Use your labeling job task type to select
     *             one of the following ARNs and use it with this parameter when you create a labeling job.
     *             Replace <code>aws-region</code> with the Amazon Web Services Region you are creating your labeling job
     *             in. For example, replace <code>aws-region</code> with <code>us-west-1</code> if you
     *             create a labeling job in US West (N. California).</p>
     *         <p>
     *             <b>Named Entity Recognition</b>
     *          </p>
     *         <p>Use the following <code>HumanTaskUiArn</code> for named entity recognition labeling
     *             jobs:</p>
     *         <p>
     *             <code>arn:aws:sagemaker:aws-region:394669845002:human-task-ui/NamedEntityRecognition</code>
     *          </p>
     *
     *         <p>
     *             <b>3D Point Cloud HumanTaskUiArns</b>
     *          </p>
     *
     *         <p>Use this <code>HumanTaskUiArn</code> for 3D point cloud object detection and 3D point
     *             cloud object detection adjustment labeling jobs. </p>
     *         <ul>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *         <p> Use this <code>HumanTaskUiArn</code> for 3D point cloud object tracking and 3D point
     *             cloud object tracking adjustment labeling jobs. </p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTracking</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *         <p> Use this <code>HumanTaskUiArn</code> for 3D point cloud semantic segmentation and 3D
     *             point cloud semantic segmentation adjustment labeling jobs.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *         <p>
     *             <b>Video Frame HumanTaskUiArns</b>
     *          </p>
     *
     *         <p>Use this <code>HumanTaskUiArn</code> for video frame object detection and video frame
     *             object detection adjustment labeling jobs. </p>
     *         <ul>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:sagemaker:region:394669845002:human-task-ui/VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *         <p> Use this <code>HumanTaskUiArn</code> for video frame object tracking and video frame
     *             object tracking adjustment labeling jobs. </p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:sagemaker:aws-region:394669845002:human-task-ui/VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *          </ul>
     */ HumanTaskUiArn ? : string ; } export namespace UiConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Information required for human workers to complete a labeling task.</p>
 */ export interface HumanTaskConfig { /**
     * <p>The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.</p>
     */ WorkteamArn : string | undefined ; /**
     * <p>Information about the user interface that workers use to complete the labeling
     *             task.</p>
     */ UiConfig : UiConfig | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of a Lambda function that is run before a data object
     *             is sent to a human worker. Use this function to provide input to a custom labeling
     *             job.</p>
     *         <p>For <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html">built-in
     *                 task types</a>, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for
     *                 <code>PreHumanTaskLambdaArn</code>. For custom labeling workflows, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates-step3.html#sms-custom-templates-step3-prelambda">Pre-annotation Lambda</a>. </p>
     *
     *
     *
     *          <p>
     *             <b>Bounding box</b> - Finds the most similar boxes from
     *                     different workers based on the Jaccard index of the boxes.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *
     *           <p>
     *             <b>Image classification</b> - Uses a variant of the Expectation
     *                     Maximization approach to estimate the true class of an image based on
     *                     annotations from individual workers.</p>
     *
     *         <ul>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *             <p>
     *             <b>Multi-label image classification</b> - Uses a variant of the Expectation
     *                     Maximization approach to estimate the true classes of an image based on
     *                     annotations from individual workers.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *          </ul>
     *
     *             <p>
     *             <b>Semantic segmentation</b> - Treats each pixel in an image as
     *                     a multi-class classification and treats pixel annotations from workers as
     *                     "votes" for the correct label.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *             <p>
     *             <b>Text classification</b> - Uses a variant of the Expectation
     *                     Maximization approach to estimate the true class of text based on annotations
     *                     from individual workers.</p>
     *             <ul>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *             <p>
     *             <b>Multi-label text classification</b> - Uses a variant of the
     *                     Expectation Maximization approach to estimate the true classes of text based on
     *                     annotations from individual workers.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel</code>
     *                </p>
     *             </li>
     *          </ul>
     *
     *             <p>
     *             <b>Named entity recognition</b> - Groups similar selections and
     *                     calculates aggregate boundaries, resolving to most-assigned label.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *
     *
     *
     *         <p>
     *             <b>Video Classification</b> - Use this task type when you need workers to classify videos using
     *             predefined labels that you specify. Workers are shown videos and are asked to choose one
     *             label for each video.</p>
     *
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClass</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *         <p>
     *             <b>Video Frame Object Detection</b> - Use this task type to
     *             have workers identify and locate objects in a sequence of video frames (images extracted
     *             from a video) using bounding boxes. For example, you can use this task to ask workers to
     *             identify and localize various objects in a series of video frames, such as cars, bikes,
     *             and pedestrians.</p>
     *
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetection</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *         <p>
     *             <b>Video Frame Object Tracking</b> - Use this task type to
     *             have workers track the movement of objects in a sequence of video frames (images
     *             extracted from a video) using bounding boxes. For example, you can use this task to ask
     *             workers to track the movement of objects, such as cars, bikes, and pedestrians. </p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *
     *
     *
     *         <p>
     *             <b>3D Point Cloud Modalities</b>
     *          </p>
     *         <p>Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks.
     *             See <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud-task-types.html">3D Point Cloud Task types
     *             </a> to learn more. </p>
     *
     *
     *         <p>
     *             <b>3D Point Cloud Object Detection</b> -
     *         Use this task type when you want workers to classify objects in a 3D point cloud by
     *         drawing 3D cuboids around objects. For example, you can use this task type to ask workers
     *         to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.</p>
     *                 <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *         <p>
     *             <b>3D Point Cloud Object Tracking</b> -
     *         Use this task type when you want workers to draw 3D cuboids around objects
     *         that appear in a sequence of 3D point cloud frames.
     *         For example, you can use this task type to ask workers to track
     *         the movement of vehicles across multiple point cloud frames.
     *         </p>
     *                 <ul>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *
     *
     *         <p>
     *             <b>3D Point Cloud Semantic Segmentation</b> -
     *             Use this task type when you want workers to create a point-level semantic segmentation masks by
     *             painting objects in a 3D point cloud using different colors where each color is assigned to one of
     *             the classes you specify.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *         <p>
     *             <b>Use the following ARNs for Label Verification and Adjustment Jobs</b>
     *          </p>
     *         <p>Use label verification and adjustment jobs to review and adjust labels. To learn more,
     *             see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html">Verify and Adjust Labels </a>.</p>
     *
     *             <p>
     *             <b>Bounding box verification</b> - Uses a variant of the
     *                 Expectation Maximization approach to estimate the true class of verification
     *                 judgement for bounding box labels based on annotations from individual
     *                 workers.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationBoundingBox</code>
     *                </p>
     *             </li>
     *          </ul>
     *
     *             <p>
     *             <b>Bounding box adjustment</b> - Finds the most similar boxes
     *                     from different workers based on the Jaccard index of the adjusted
     *                     annotations.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *             <p>
     *             <b>Semantic segmentation verification</b> - Uses a variant of
     *                     the Expectation Maximization approach to estimate the true class of verification
     *                     judgment for semantic segmentation labels based on annotations from individual
     *                     workers.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *             <p>
     *             <b>Semantic segmentation adjustment</b> - Treats each pixel in
     *                     an image as a multi-class classification and treats pixel adjusted annotations
     *                     from workers as "votes" for the correct label.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *
     *
     *
     *         <p>
     *             <b>Video Frame Object Detection Adjustment</b> -
     *             Use this task type when you want workers to adjust bounding boxes that workers have added
     *             to video frames to classify and localize objects in a sequence of video frames.</p>
     *
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetection</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *         <p>
     *             <b>Video Frame Object Tracking Adjustment</b> -
     *             Use this task type when you want workers to adjust bounding boxes that workers have added
     *             to video frames to track object movement across a sequence of video frames.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking</code>
     *                 </p>
     *             </li>
     *          </ul>
     *
     *
     *
     *
     *         <p>
     *             <b>3D point cloud object detection adjustment</b> - Adjust
     *             3D cuboids in a point cloud frame. </p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection</code>
     *                </p>
     *             </li>
     *          </ul>
     *
     *         <p>
     *             <b>3D point cloud object tracking adjustment</b> - Adjust 3D
     *             cuboids across a sequence of point cloud frames. </p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking</code>
     *                </p>
     *             </li>
     *          </ul>
     *
     *         <p>
     *             <b>3D point cloud semantic segmentation adjustment</b> -
     *             Adjust semantic segmentation masks in a 3D point cloud. </p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation</code>
     *                </p>
     *             </li>
     *          </ul>
     */ PreHumanTaskLambdaArn : string | undefined ; /**
     * <p>Keywords used to describe the task so that workers on Amazon Mechanical Turk can
     *             discover the task.</p>
     */ TaskKeywords ? : string [ ] ; /**
     * <p>A title for the task for your human workers.</p>
     */ TaskTitle : string | undefined ; /**
     * <p>A description of the task for your human workers.</p>
     */ TaskDescription : string | undefined ; /**
     * <p>The number of human workers that will label an object. </p>
     */ NumberOfHumanWorkersPerDataObject : number | undefined ; /**
     * <p>The amount of time that a worker has to complete a task. </p>
     *         <p>If you create a custom labeling job, the maximum value for this parameter is 8 hours
     *             (28,800 seconds).</p>
     *         <p>If you create a labeling job using a <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html">built-in task type</a> the maximum
     *             for this parameter depends on the task type you use:</p>
     *         <ul>
     *             <li>
     *                 <p>For <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-label-images.html">image</a> and
     *                     <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-label-text.html">text</a> labeling jobs,
     *                     the maximum is 8 hours (28,800 seconds).</p>
     *             </li>
     *             <li>
     *                 <p>For <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud.html">3D point cloud</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-video.html">video frame</a> labeling jobs, the maximum is 30 days (2952,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.</p>
     *             </li>
     *          </ul>
     */ TaskTimeLimitInSeconds : number | undefined ; /**
     * <p>The length of time that a task remains available for labeling by human workers. The
     *             default and maximum values for this parameter depend on the type of workforce you
     *             use.</p>
     *         <ul>
     *             <li>
     *                 <p>If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours (43,200 seconds).
     *                     The default is 6 hours (21,600 seconds).</p>
     *             </li>
     *             <li>
     *                 <p>If you choose a private or vendor workforce, the default value is 30 days (2592,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.</p>
     *             </li>
     *          </ul>
     */ TaskAvailabilityLifetimeInSeconds ? : number ; /**
     * <p>Defines the maximum number of data objects that can be labeled by human workers at the
     *             same time. Also referred to as batch size. Each object may have more than one worker at one time.
     *             The default value is 1000 objects.</p>
     */ MaxConcurrentTaskCount ? : number ; /**
     * <p>Configures how labels are consolidated across human workers.</p>
     */ AnnotationConsolidationConfig : AnnotationConsolidationConfig | undefined ; /**
     * <p>The price that you pay for each task performed by an Amazon Mechanical Turk worker.</p>
     */ PublicWorkforceTaskPrice ? : PublicWorkforceTaskPrice ; } export namespace HumanTaskConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Attributes of the data specified by the customer. Use these to describe the data to be
 *             labeled.</p>
 */ export interface LabelingJobDataAttributes { /**
     * <p>Declares that your content is free of personally identifiable information or adult
     *             content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task
     *             based on this information.</p>
     */ ContentClassifiers ? : ( ContentClassifier | string ) [ ] ; } export namespace LabelingJobDataAttributes { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The Amazon S3 location of the input data objects.</p>
 */ export interface LabelingJobS3DataSource { /**
     * <p>The Amazon S3 location of the manifest file that describes the input data objects. </p>
     *         <p>The input manifest file referenced in <code>ManifestS3Uri</code> must contain one of
     *             the following keys: <code>source-ref</code> or <code>source</code>. The value of the
     *             keys are interpreted as follows:</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>source-ref</code>: The source of the object is the Amazon S3 object
     *                     specified in the value. Use this value when the object is a binary object, such
     *                     as an image.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>source</code>: The source of the object is the value. Use this
     *                     value when the object is a text value.</p>
     *             </li>
     *          </ul>
     *         <p>If you are a new user of Ground Truth, it is recommended you review <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-input-data-input-manifest.html">Use an Input Manifest File </a> in the Amazon SageMaker Developer Guide to learn how to
     *             create an input manifest file.</p>
     */ ManifestS3Uri : string | undefined ; } export namespace LabelingJobS3DataSource { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>An Amazon SNS data source used for streaming labeling jobs.</p>
 */ export interface LabelingJobSnsDataSource { /**
     * <p>The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic
     *       you will use to send new data objects to a streaming labeling job.</p>
     */ SnsTopicArn : string | undefined ; } export namespace LabelingJobSnsDataSource { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Provides information about the location of input data.</p>
 *
 *          <p>You must specify at least one of the following: <code>S3DataSource</code> or <code>SnsDataSource</code>.</p>
 *          <p>Use <code>SnsDataSource</code> to specify an SNS input topic
 *     for a streaming labeling job. If you do not specify
 *     and SNS input topic ARN, Ground Truth will create a one-time labeling job.</p>
 *          <p>Use <code>S3DataSource</code> to specify an input
 *     manifest file for both streaming and one-time labeling jobs.
 *     Adding an <code>S3DataSource</code> is optional if you use <code>SnsDataSource</code> to create a streaming labeling job.</p>
 */ export interface LabelingJobDataSource { /**
     * <p>The Amazon S3 location of the input data objects.</p>
     */ S3DataSource ? : LabelingJobS3DataSource ; /**
     * <p>An Amazon SNS data source used for streaming labeling jobs. To learn more, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-streaming-labeling-job.html#sms-streaming-how-it-works-send-data">Send Data to a Streaming Labeling Job</a>. </p>
     */ SnsDataSource ? : LabelingJobSnsDataSource ; } export namespace LabelingJobDataSource { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Input configuration information for a labeling job.</p>
 */ export interface LabelingJobInputConfig { /**
     * <p>The location of the input data.</p>
     */ DataSource : LabelingJobDataSource | undefined ; /**
     * <p>Attributes of the data specified by the customer.</p>
     */ DataAttributes ? : LabelingJobDataAttributes ; } export namespace LabelingJobInputConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configure encryption on the storage volume attached to the ML compute instance used to
 *             run automated data labeling model training and inference. </p>
 */ export interface LabelingJobResourceConfig { /**
     * <p>The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume
     *             attached to the ML compute instance(s) that run the training and inference jobs used for
     *             automated data labeling. </p>
     *         <p>You can only specify a <code>VolumeKmsKeyId</code> when you create a labeling job with
     *             automated data labeling enabled using the API operation <code>CreateLabelingJob</code>.
     *             You cannot specify an Amazon Web Services KMS key to encrypt the storage volume used for
     *             automated data labeling model training and inference when you create a labeling job
     *             using the console. To learn more, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-security.html">Output Data and Storage Volume
     *                 Encryption</a>.</p>
     *         <p>The <code>VolumeKmsKeyId</code> can be any of the following formats:</p>
     *         <ul>
     *             <li>
     *                 <p>KMS Key ID</p>
     *                 <p>
     *                     <code>"1234abcd-12ab-34cd-56ef-1234567890ab"</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>Amazon Resource Name (ARN) of a KMS Key</p>
     *                 <p>
     *                     <code>"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"</code>
     *                 </p>
     *             </li>
     *          </ul>
     */ VolumeKmsKeyId ? : string ; } export namespace LabelingJobResourceConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Provides configuration information for auto-labeling of your data objects. A
 *                 <code>LabelingJobAlgorithmsConfig</code> object must be supplied in order to use
 *             auto-labeling.</p>
 */ export interface LabelingJobAlgorithmsConfig { /**
     * <p>Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You
     *             must select one of the following ARNs:</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                     <i>Image classification</i>
     *                 </p>
     *                 <p>
     *                     <code>arn:aws:sagemaker:<i>region</i>:027400017018:labeling-job-algorithm-specification/image-classification</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <i>Text classification</i>
     *                 </p>
     *                 <p>
     *                     <code>arn:aws:sagemaker:<i>region</i>:027400017018:labeling-job-algorithm-specification/text-classification</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <i>Object detection</i>
     *                 </p>
     *                 <p>
     *                     <code>arn:aws:sagemaker:<i>region</i>:027400017018:labeling-job-algorithm-specification/object-detection</code>
     *                 </p>
     *             </li>
     *             <li>
     *                 <p>
     *                     <i>Semantic Segmentation</i>
     *                 </p>
     *                 <p>
     *                     <code>arn:aws:sagemaker:<i>region</i>:027400017018:labeling-job-algorithm-specification/semantic-segmentation</code>
     *                 </p>
     *             </li>
     *          </ul>
     */ LabelingJobAlgorithmSpecificationArn : string | undefined ; /**
     * <p>At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final
     *             model used for auto-labeling. You can use this model as the starting point for
     *             subsequent similar jobs by providing the ARN of the model here. </p>
     */ InitialActiveLearningModelArn ? : string ; /**
     * <p>Provides configuration information for a labeling job.</p>
     */ LabelingJobResourceConfig ? : LabelingJobResourceConfig ; } export namespace LabelingJobAlgorithmsConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Output configuration information for a labeling job.</p>
 */ export interface LabelingJobOutputConfig { /**
     * <p>The Amazon S3 location to write output data.</p>
     */ S3OutputPath : string | undefined ; /**
     * <p>The Amazon Web Services Key Management Service ID of the key used to encrypt the output data, if any.</p>
     *         <p>If you provide your own KMS key ID, you must add the required permissions to your KMS
     *             key described in <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-security-permission.html#sms-security-kms-permissions">Encrypt Output Data and Storage Volume with Amazon Web Services KMS</a>.</p>
     *         <p>If you don't provide a KMS key ID, Amazon SageMaker uses the default Amazon Web Services KMS key for Amazon S3 for your
     *             role's account to encrypt your output data.</p>
     *         <p>If you use a bucket policy with an <code>s3:PutObject</code> permission that only
     *             allows objects with server-side encryption, set the condition key of
     *                 <code>s3:x-amz-server-side-encryption</code> to <code>"aws:kms"</code>. For more
     *             information, see <a href="https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html">KMS-Managed Encryption Keys</a> in the <i>Amazon Simple Storage Service Developer
     *                 Guide.</i>
     *          </p>
     */ KmsKeyId ? : string ; /**
     * <p>An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a <code>SnsTopicArn</code> if you want to
     *             do real time chaining to another streaming job and receive an Amazon SNS notifications each
     *             time a data object is submitted by a worker.</p>
     *          <p>If you provide an <code>SnsTopicArn</code> in <code>OutputConfig</code>, when workers
     *             complete labeling tasks, Ground Truth will send labeling task output data to the SNS output
     *             topic you specify here. </p>
     *         <p>To learn more, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-streaming-labeling-job.html#sms-streaming-how-it-works-output-data">Receive Output Data from a Streaming Labeling
     *                 Job</a>. </p>
     */ SnsTopicArn ? : string ; } export namespace LabelingJobOutputConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>A set of conditions for stopping a labeling job. If any of the conditions are met, the
 *             job is automatically stopped. You can use these conditions to control the cost of data
 *             labeling.</p>
 *         <note>
 *             <p>Labeling jobs fail after 30 days with an appropriate client error message.</p>
 *         </note>
 */ export interface LabelingJobStoppingConditions { /**
     * <p>The maximum number of objects that can be labeled by human workers.</p>
     */ MaxHumanLabeledObjectCount ? : number ; /**
     * <p>The maximum number of input data objects that should be labeled.</p>
     */ MaxPercentageOfInputDatasetLabeled ? : number ; } export namespace LabelingJobStoppingConditions { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateLabelingJobRequest { /**
     * <p>The name of the labeling job. This name is used to identify the job in a list of
     *             labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region.
     *                 <code>LabelingJobName</code> is not case sensitive. For example, Example-job and
     *             example-job are considered the same labeling job name by Ground Truth.</p>
     */ LabelingJobName : string | undefined ; /**
     * <p>The attribute name to use for the label in the output manifest file. This is the key
     *             for the key/value pair formed with the label that a worker assigns to the object. The
     *                 <code>LabelAttributeName</code> must meet the following requirements.</p>
     *         <ul>
     *             <li>
     *                 <p>The name can't end with "-metadata". </p>
     *             </li>
     *             <li>
     *                 <p>If you are using one of the following <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html">built-in task types</a>,
     *                     the attribute name <i>must</i> end with "-ref". If the task type
     *                     you are using is not listed below, the attribute name <i>must
     *                         not</i> end with "-ref".</p>
     *                 <ul>
     *                   <li>
     *                         <p>Image semantic segmentation (<code>SemanticSegmentation)</code>, and
     *                             adjustment (<code>AdjustmentSemanticSegmentation</code>) and
     *                             verification (<code>VerificationSemanticSegmentation</code>) labeling
     *                             jobs for this task type.</p>
     *                     </li>
     *                   <li>
     *                         <p>Video frame object detection (<code>VideoObjectDetection</code>), and
     *                             adjustment and verification
     *                             (<code>AdjustmentVideoObjectDetection</code>) labeling jobs for this
     *                             task type.</p>
     *                     </li>
     *                   <li>
     *                         <p>Video frame object tracking (<code>VideoObjectTracking</code>), and
     *                             adjustment and verification (<code>AdjustmentVideoObjectTracking</code>)
     *                             labeling jobs for this task type.</p>
     *                     </li>
     *                   <li>
     *                         <p>3D point cloud semantic segmentation
     *                                 (<code>3DPointCloudSemanticSegmentation</code>), and adjustment and
     *                             verification (<code>Adjustment3DPointCloudSemanticSegmentation</code>)
     *                             labeling jobs for this task type. </p>
     *                     </li>
     *                   <li>
     *                         <p>3D point cloud object tracking
     *                                 (<code>3DPointCloudObjectTracking</code>), and adjustment and
     *                             verification (<code>Adjustment3DPointCloudObjectTracking</code>)
     *                             labeling jobs for this task type. </p>
     *                     </li>
     *                </ul>
     *             </li>
     *          </ul>
     *         <p></p>
     *         <important>
     *             <p>If you are creating an adjustment or verification labeling job, you must use a
     *                     <i>different</i>
     *                 <code>LabelAttributeName</code> than the one used in the original labeling job. The
     *                 original labeling job is the Ground Truth labeling job that produced the labels that you
     *                 want verified or adjusted. To learn more about adjustment and verification labeling
     *                 jobs, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html">Verify and Adjust
     *                     Labels</a>.</p>
     *         </important>
     */ LabelAttributeName : string | undefined ; /**
     * <p>Input data for the labeling job, such as the Amazon S3 location of the data objects and the
     *             location of the manifest file that describes the data objects.</p>
     *         <p>You must specify at least one of the following: <code>S3DataSource</code> or
     *                 <code>SnsDataSource</code>. </p>
     *         <ul>
     *             <li>
     *                 <p>Use <code>SnsDataSource</code> to specify an SNS input topic for a streaming
     *                     labeling job. If you do not specify and SNS input topic ARN, Ground Truth will
     *                     create a one-time labeling job that stops after all data objects in the input
     *                     manifest file have been labeled.</p>
     *             </li>
     *             <li>
     *                 <p>Use <code>S3DataSource</code> to specify an input manifest file for both
     *                     streaming and one-time labeling jobs. Adding an <code>S3DataSource</code> is
     *                     optional if you use <code>SnsDataSource</code> to create a streaming labeling
     *                     job.</p>
     *             </li>
     *          </ul>
     *         <p>If you use the Amazon Mechanical Turk workforce, your input data should not include
     *             confidential information, personal information or protected health information. Use
     *                 <code>ContentClassifiers</code> to specify that your data is free of personally
     *             identifiable information and adult content.</p>
     */ InputConfig : LabelingJobInputConfig | undefined ; /**
     * <p>The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt
     *             the output data, if any.</p>
     */ OutputConfig : LabelingJobOutputConfig | undefined ; /**
     * <p>The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf
     *             during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker
     *             can successfully complete data labeling.</p>
     */ RoleArn : string | undefined ; /**
     * <p>The S3 URI of the file, referred to as a <i>label category configuration
     *                 file</i>, that defines the categories used to label the data objects.</p>
     *         <p>For 3D point cloud and video frame task types, you can add label category attributes
     *             and frame attributes to your label category configuration file. To learn how, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud-label-category-config.html">Create a
     *                 Labeling Category Configuration File for 3D Point Cloud Labeling Jobs</a>. </p>
     *         <p>For named entity recognition jobs, in addition to <code>"labels"</code>, you must
     *             provide worker instructions in the label category configuration file using the
     *                 <code>"instructions"</code> parameter: <code>"instructions":
     *                 {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>",
     *                 "fullInstruction":"<p>Add additional instructions.</p>"}</code>. For details
     *             and an example, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-named-entity-recg.html#sms-creating-ner-api">Create a
     *                 Named Entity Recognition Labeling Job (API) </a>.</p>
     *         <p>For all other <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html">built-in task types</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates.html">custom
     *                 tasks</a>, your label category configuration file must be a JSON file in the
     *             following format. Identify the labels you want to use by replacing <code>label_1</code>,
     *                 <code>label_2</code>,<code>...</code>,<code>label_n</code> with your label
     *             categories.</p>
     *         <p>
     *             <code>{ </code>
     *          </p>
     *         <p>
     *             <code>"document-version": "2018-11-28",</code>
     *          </p>
     *         <p>
     *             <code>"labels": [{"label": "label_1"},{"label": "label_2"},...{"label":
     *                 "label_n"}]</code>
     *          </p>
     *         <p>
     *             <code>}</code>
     *          </p>
     *         <p>Note the following about the label category configuration file:</p>
     *         <ul>
     *             <li>
     *                 <p>For image classification and text classification (single and multi-label) you
     *                     must specify at least two label categories. For all other task types, the
     *                     minimum number of label categories required is one. </p>
     *             </li>
     *             <li>
     *                 <p>Each label category must be unique, you cannot specify duplicate label
     *                     categories.</p>
     *             </li>
     *             <li>
     *                 <p>If you create a 3D point cloud or video frame adjustment or verification
     *                     labeling job, you must include <code>auditLabelAttributeName</code> in the label
     *                     category configuration. Use this parameter to enter the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName">
     *                      <code>LabelAttributeName</code>
     *                   </a> of the labeling job you want to
     *                     adjust or verify annotations of.</p>
     *             </li>
     *          </ul>
     */ LabelCategoryConfigS3Uri ? : string ; /**
     * <p>A set of conditions for stopping the labeling job. If any of the conditions are met,
     *             the job is automatically stopped. You can use these conditions to control the cost of
     *             data labeling.</p>
     */ StoppingConditions ? : LabelingJobStoppingConditions ; /**
     * <p>Configures the information required to perform automated data labeling.</p>
     */ LabelingJobAlgorithmsConfig ? : LabelingJobAlgorithmsConfig ; /**
     * <p>Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).</p>
     */ HumanTaskConfig : HumanTaskConfig | undefined ; /**
     * <p>An array of key/value pairs. For more information, see <a href="https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what">Using Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management
     *                 User Guide</i>.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateLabelingJobRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateLabelingJobResponse { /**
     * <p>The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the
     *             labeling job.</p>
     */ LabelingJobArn : string | undefined ; } export namespace CreateLabelingJobResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum InferenceExecutionMode { DIRECT = "Direct" , SERIAL = "Serial" } /**
 * <p>Specifies details about how containers in a multi-container endpoint are run.</p>
 */ export interface InferenceExecutionConfig { /**
     * <p>How containers in a multi-container are run. The following values are valid.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>SERIAL</code> - Containers run as a serial pipeline.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>DIRECT</code> - Only the individual container that you specify is
     *                     run.</p>
     *             </li>
     *          </ul>
     */ Mode : InferenceExecutionMode | string | undefined ; } export namespace InferenceExecutionConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelInput { /**
     * <p>The name of the new model.</p>
     */ ModelName : string | undefined ; /**
     * <p>The location of the primary docker image containing inference code, associated
     *             artifacts, and custom environment map that the inference code uses when the model is
     *             deployed for predictions. </p>
     */ PrimaryContainer ? : ContainerDefinition ; /**
     * <p>Specifies the containers in the inference pipeline.</p>
     */ Containers ? : ContainerDefinition [ ] ; /**
     * <p>Specifies details of how containers in a multi-container endpoint are called.</p>
     */ InferenceExecutionConfig ? : InferenceExecutionConfig ; /**
     * <p>The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model
     *             artifacts and docker image for deployment on ML compute instances or for batch transform
     *             jobs. Deploying on ML compute instances is part of model hosting. For more information,
     *             see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">Amazon SageMaker
     *                 Roles</a>. </p>
     *         <note>
     *             <p>To be able to pass this role to Amazon SageMaker, the caller of this API must have the
     *                     <code>iam:PassRole</code> permission.</p>
     *         </note>
     */ ExecutionRoleArn : string | undefined ; /**
     * <p>An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in
     *             different ways, for example, by purpose, owner, or environment. For more information,
     *             see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services
     *                 Resources</a>.</p>
     */ Tags ? : Tag [ ] ; /**
     * <p>A <a>VpcConfig</a> object that specifies the VPC that you want your model
     *             to connect to. Control access to and from your model container by configuring the VPC.
     *                 <code>VpcConfig</code> is used in hosting services and in batch transform. For more
     *             information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html">Protect Endpoints by Using an Amazon Virtual Private Cloud</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-vpc.html">Protect Data in Batch
     *                 Transform Jobs by Using an Amazon Virtual Private Cloud</a>.</p>
     */ VpcConfig ? : VpcConfig ; /**
     * <p>Isolates the model container. No inbound or outbound network calls can be made to or
     *             from the model container.</p>
     */ EnableNetworkIsolation ? : boolean ; } export namespace CreateModelInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelOutput { /**
     * <p>The ARN of the model created in Amazon SageMaker.</p>
     */ ModelArn : string | undefined ; } export namespace CreateModelOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Docker container image configuration object for the model bias job.</p>
 */ export interface ModelBiasAppSpecification { /**
     * <p>The container image to be run by the model bias job.</p>
     */ ImageUri : string | undefined ; /**
     * <p>JSON formatted S3 file that defines bias parameters. For more information on this JSON
     *          configuration file, see <a href="https://docs.aws.amazon.com/sagemaker/latest/json-bias-parameter-config.html">Configure bias
     *          parameters</a>.</p>
     */ ConfigUri : string | undefined ; /**
     * <p>Sets the environment variables in the Docker container.</p>
     */ Environment ? : { [ key ] : string ; } ; } export namespace ModelBiasAppSpecification { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The configuration for a baseline model bias job.</p>
 */ export interface ModelBiasBaselineConfig { /**
     * <p>The name of the baseline model bias job.</p>
     */ BaseliningJobName ? : string ; /**
     * <p>The constraints resource for a monitoring job.</p>
     */ ConstraintsResource ? : MonitoringConstraintsResource ; } export namespace ModelBiasBaselineConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The ground truth labels for the dataset used for the monitoring job.</p>
 */ export interface MonitoringGroundTruthS3Input { /**
     * <p>The address of the Amazon S3 location of the ground truth labels.</p>
     */ S3Uri ? : string ; } export namespace MonitoringGroundTruthS3Input { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Inputs for the model bias job.</p>
 */ export interface ModelBiasJobInput { /**
     * <p>Input object for the endpoint</p>
     */ EndpointInput : EndpointInput | undefined ; /**
     * <p>Location of ground truth labels to use in model bias job.</p>
     */ GroundTruthS3Input : MonitoringGroundTruthS3Input | undefined ; } export namespace ModelBiasJobInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelBiasJobDefinitionRequest { /**
     * <p>The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the
     *          Amazon Web Services account.</p>
     */ JobDefinitionName : string | undefined ; /**
     * <p>The baseline configuration for a model bias job.</p>
     */ ModelBiasBaselineConfig ? : ModelBiasBaselineConfig ; /**
     * <p>Configures the model bias job to run a specified Docker container image.</p>
     */ ModelBiasAppSpecification : ModelBiasAppSpecification | undefined ; /**
     * <p>Inputs for the model bias job.</p>
     */ ModelBiasJobInput : ModelBiasJobInput | undefined ; /**
     * <p>The output configuration for monitoring jobs.</p>
     */ ModelBiasJobOutputConfig : MonitoringOutputConfig | undefined ; /**
     * <p>Identifies the resources to deploy for a monitoring job.</p>
     */ JobResources : MonitoringResources | undefined ; /**
     * <p>Networking options for a model bias job.</p>
     */ NetworkConfig ? : MonitoringNetworkConfig ; /**
     * <p>The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to
     *          perform tasks on your behalf.</p>
     */ RoleArn : string | undefined ; /**
     * <p>A time limit for how long the monitoring job is allowed to run before stopping.</p>
     */ StoppingCondition ? : MonitoringStoppingCondition ; /**
     * <p>(Optional) An array of key-value pairs. For more information, see <a href="https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL">Using Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management
     *             User Guide</i>.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateModelBiasJobDefinitionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelBiasJobDefinitionResponse { /**
     * <p>The Amazon Resource Name (ARN) of the model bias job.</p>
     */ JobDefinitionArn : string | undefined ; } export namespace CreateModelBiasJobDefinitionResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Docker container image configuration object for the model explainability job.</p>
 */ export interface ModelExplainabilityAppSpecification { /**
     * <p>The container image to be run by the model explainability job.</p>
     */ ImageUri : string | undefined ; /**
     * <p>JSON formatted S3 file that defines explainability parameters. For more information on
     *          this JSON configuration file, see <a href="https://docs.aws.amazon.com/sagemaker/latest/json-model-explainability-parameter-config.html">Configure model
     *             explainability parameters</a>.</p>
     */ ConfigUri : string | undefined ; /**
     * <p>Sets the environment variables in the Docker container.</p>
     */ Environment ? : { [ key ] : string ; } ; } export namespace ModelExplainabilityAppSpecification { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The configuration for a baseline model explainability job.</p>
 */ export interface ModelExplainabilityBaselineConfig { /**
     * <p>The name of the baseline model explainability job.</p>
     */ BaseliningJobName ? : string ; /**
     * <p>The constraints resource for a monitoring job.</p>
     */ ConstraintsResource ? : MonitoringConstraintsResource ; } export namespace ModelExplainabilityBaselineConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Inputs for the model explainability job.</p>
 */ export interface ModelExplainabilityJobInput { /**
     * <p>Input object for the endpoint</p>
     */ EndpointInput : EndpointInput | undefined ; } export namespace ModelExplainabilityJobInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelExplainabilityJobDefinitionRequest { /**
     * <p> The name of the model explainability job definition. The name must be unique within an
     *          Amazon Web Services Region in the Amazon Web Services account.</p>
     */ JobDefinitionName : string | undefined ; /**
     * <p>The baseline configuration for a model explainability job.</p>
     */ ModelExplainabilityBaselineConfig ? : ModelExplainabilityBaselineConfig ; /**
     * <p>Configures the model explainability job to run a specified Docker container
     *          image.</p>
     */ ModelExplainabilityAppSpecification : ModelExplainabilityAppSpecification | undefined ; /**
     * <p>Inputs for the model explainability job.</p>
     */ ModelExplainabilityJobInput : ModelExplainabilityJobInput | undefined ; /**
     * <p>The output configuration for monitoring jobs.</p>
     */ ModelExplainabilityJobOutputConfig : MonitoringOutputConfig | undefined ; /**
     * <p>Identifies the resources to deploy for a monitoring job.</p>
     */ JobResources : MonitoringResources | undefined ; /**
     * <p>Networking options for a model explainability job.</p>
     */ NetworkConfig ? : MonitoringNetworkConfig ; /**
     * <p>The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to
     *          perform tasks on your behalf.</p>
     */ RoleArn : string | undefined ; /**
     * <p>A time limit for how long the monitoring job is allowed to run before stopping.</p>
     */ StoppingCondition ? : MonitoringStoppingCondition ; /**
     * <p>(Optional) An array of key-value pairs. For more information, see <a href="https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL">Using Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management
     *             User Guide</i>.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateModelExplainabilityJobDefinitionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelExplainabilityJobDefinitionResponse { /**
     * <p>The Amazon Resource Name (ARN) of the model explainability job.</p>
     */ JobDefinitionArn : string | undefined ; } export namespace CreateModelExplainabilityJobDefinitionResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Contains details regarding the file source.</p>
 */ export interface FileSource { /**
     * <p>The type of content stored in the file source.</p>
     */ ContentType ? : string ; /**
     * <p>The digest of the file source.</p>
     */ ContentDigest ? : string ; /**
     * <p>The Amazon S3 URI for the file source.</p>
     */ S3Uri : string | undefined ; } export namespace FileSource { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Represents the drift check bias baselines that can be used when the model monitor is set using the
 *          model package.</p>
 */ export interface DriftCheckBias { /**
     * <p>The bias config file for a model.</p>
     */ ConfigFile ? : FileSource ; /**
     * <p></p>
     */ PreTrainingConstraints ? : MetricsSource ; /**
     * <p></p>
     */ PostTrainingConstraints ? : MetricsSource ; } export namespace DriftCheckBias { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Represents the drift check explainability baselines that can be used when the model monitor is set
 *          using the model package. </p>
 */ export interface DriftCheckExplainability { /**
     * <p></p>
     */ Constraints ? : MetricsSource ; /**
     * <p>The explainability config file for the model.</p>
     */ ConfigFile ? : FileSource ; } export namespace DriftCheckExplainability { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Represents the drift check data quality baselines that can be used when the model monitor is set using
 *          the model package. </p>
 */ export interface DriftCheckModelDataQuality { /**
     * <p></p>
     */ Statistics ? : MetricsSource ; /**
     * <p></p>
     */ Constraints ? : MetricsSource ; } export namespace DriftCheckModelDataQuality { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Represents the drift check model quality baselines that can be used when the model monitor is set using
 *          the model package. </p>
 */ export interface DriftCheckModelQuality { /**
     * <p></p>
     */ Statistics ? : MetricsSource ; /**
     * <p></p>
     */ Constraints ? : MetricsSource ; } export namespace DriftCheckModelQuality { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Represents the drift check baselines that can be used when the model monitor is set using the model
 *          package. </p>
 */ export interface DriftCheckBaselines { /**
     * <p>Represents the drift check bias baselines that can be used when the model monitor is set using the model
     *          package. </p>
     */ Bias ? : DriftCheckBias ; /**
     * <p>Represents the drift check explainability baselines that can be used when the model monitor is set using
     *          the model package. </p>
     */ Explainability ? : DriftCheckExplainability ; /**
     * <p>Represents the drift check model quality baselines that can be used when the model monitor is set using
     *          the model package.</p>
     */ ModelQuality ? : DriftCheckModelQuality ; /**
     * <p>Represents the drift check model data quality baselines that can be used when the model monitor is set
     *          using the model package.</p>
     */ ModelDataQuality ? : DriftCheckModelDataQuality ; } export namespace DriftCheckBaselines { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Contains explainability metrics for a model.</p>
 */ export interface Explainability { /**
     * <p>The explainability report for a model.</p>
     */ Report ? : MetricsSource ; } export namespace Explainability { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Data quality constraints and statistics for a model.</p>
 */ export interface ModelDataQuality { /**
     * <p>Data quality statistics for a model.</p>
     */ Statistics ? : MetricsSource ; /**
     * <p>Data quality constraints for a model.</p>
     */ Constraints ? : MetricsSource ; } export namespace ModelDataQuality { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Model quality statistics and constraints.</p>
 */ export interface ModelQuality { /**
     * <p>Model quality statistics.</p>
     */ Statistics ? : MetricsSource ; /**
     * <p>Model quality constraints.</p>
     */ Constraints ? : MetricsSource ; } export namespace ModelQuality { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Contains metrics captured from a model.</p>
 */ export interface ModelMetrics { /**
     * <p>Metrics that measure the quality of a model.</p>
     */ ModelQuality ? : ModelQuality ; /**
     * <p>Metrics that measure the quality of the input data for a model.</p>
     */ ModelDataQuality ? : ModelDataQuality ; /**
     * <p>Metrics that measure bais in a model.</p>
     */ Bias ? : Bias ; /**
     * <p>Metrics that help explain a model.</p>
     */ Explainability ? : Explainability ; } export namespace ModelMetrics { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Specifies an algorithm that was used to create the model package. The algorithm must
 *             be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you
 *             are subscribed to.</p>
 */ export interface SourceAlgorithm { /**
     * <p>The Amazon S3 path where the model artifacts, which result from model training, are stored.
     *             This path must point to a single <code>gzip</code> compressed tar archive
     *                 (<code>.tar.gz</code> suffix).</p>
     *         <note>
     *             <p>The model artifacts must be in an S3 bucket that is in the same region as the
     *                 algorithm.</p>
     *         </note>
     */ ModelDataUrl ? : string ; /**
     * <p>The name of an algorithm that was used to create the model package. The algorithm must
     *             be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you
     *             are subscribed to.</p>
     */ AlgorithmName : string | undefined ; } export namespace SourceAlgorithm { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>A list of algorithms that were used to create a model package.</p>
 */ export interface SourceAlgorithmSpecification { /**
     * <p>A list of the algorithms that were used to create a model package.</p>
     */ SourceAlgorithms : SourceAlgorithm [ ] | undefined ; } export namespace SourceAlgorithmSpecification { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Contains data, such as the inputs and targeted instance types that are used in the
 *             process of validating the model package.</p>
 *         <p>The data provided in the validation profile is made available to your buyers on Amazon Web Services
 *             Marketplace.</p>
 */ export interface ModelPackageValidationProfile { /**
     * <p>The name of the profile for the model package.</p>
     */ ProfileName : string | undefined ; /**
     * <p>The <code>TransformJobDefinition</code> object that describes the transform job used
     *             for the validation of the model package.</p>
     */ TransformJobDefinition : TransformJobDefinition | undefined ; } export namespace ModelPackageValidationProfile { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Specifies batch transform jobs that Amazon SageMaker runs to validate your model package.</p>
 */ export interface ModelPackageValidationSpecification { /**
     * <p>The IAM roles to be used for the validation of the model package.</p>
     */ ValidationRole : string | undefined ; /**
     * <p>An array of <code>ModelPackageValidationProfile</code> objects, each of which
     *             specifies a batch transform job that Amazon SageMaker runs to validate your model package.</p>
     */ ValidationProfiles : ModelPackageValidationProfile [ ] | undefined ; } export namespace ModelPackageValidationSpecification { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelPackageInput { /**
     * <p>The name of the model package. The name must have 1 to 63 characters. Valid characters
     *             are a-z, A-Z, 0-9, and - (hyphen).</p>
     *         <p>This parameter is required for unversioned models. It is not applicable to versioned
     *             models.</p>
     */ ModelPackageName ? : string ; /**
     * <p>The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.</p>
     *         <p>This parameter is required for versioned models, and does not apply to unversioned
     *             models.</p>
     */ ModelPackageGroupName ? : string ; /**
     * <p>A description of the model package.</p>
     */ ModelPackageDescription ? : string ; /**
     * <p>Specifies details about inference jobs that can be run with models based on this model
     *             package, including the following:</p>
     *         <ul>
     *             <li>
     *                 <p>The Amazon ECR paths of containers that contain the inference code and model
     *                     artifacts.</p>
     *             </li>
     *             <li>
     *                 <p>The instance types that the model package supports for transform jobs and
     *                     real-time endpoints used for inference.</p>
     *             </li>
     *             <li>
     *                 <p>The input and output content formats that the model package supports for
     *                     inference.</p>
     *             </li>
     *          </ul>
     */ InferenceSpecification ? : InferenceSpecification ; /**
     * <p>Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the
     *             model package.</p>
     */ ValidationSpecification ? : ModelPackageValidationSpecification ; /**
     * <p>Details about the algorithm that was used to create the model package.</p>
     */ SourceAlgorithmSpecification ? : SourceAlgorithmSpecification ; /**
     * <p>Whether to certify the model package for listing on Amazon Web Services Marketplace.</p>
     *         <p>This parameter is optional for unversioned models, and does not apply to versioned
     *             models.</p>
     */ CertifyForMarketplace ? : boolean ; /**
     * <p>A list of key value pairs associated with the model. For more information, see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services
     *             resources</a> in the <i>Amazon Web Services General Reference Guide</i>.</p>
     */ Tags ? : Tag [ ] ; /**
     * <p>Whether the model is approved for deployment.</p>
     *         <p>This parameter is optional for versioned models, and does not apply to unversioned
     *             models.</p>
     *         <p>For versioned models, the value of this parameter must be set to <code>Approved</code>
     *         to deploy the model.</p>
     */ ModelApprovalStatus ? : ModelApprovalStatus | string ; /**
     * <p>Metadata properties of the tracking entity, trial, or trial component.</p>
     */ MetadataProperties ? : MetadataProperties ; /**
     * <p>A structure that contains model metrics reports.</p>
     */ ModelMetrics ? : ModelMetrics ; /**
     * <p>A unique token that guarantees that the call to this API is idempotent.</p>
     */ ClientToken ? : string ; /**
     * <p>The metadata properties associated with the model package versions.</p>
     */ CustomerMetadataProperties ? : { [ key ] : string ; } ; /**
     * <p>Represents the drift check baselines that can be used when the model monitor is set using the model package.
     *          For more information, see the topic on <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-quality-clarify-baseline-lifecycle.html#pipelines-quality-clarify-baseline-drift-detection">Drift Detection against Previous Baselines in SageMaker Pipelines</a> in the <i>Amazon SageMaker Developer Guide</i>.
     *       </p>
     */ DriftCheckBaselines ? : DriftCheckBaselines ; /**
     * <p>The machine learning domain of your model package and its components. Common
     *     machine learning domains include computer vision and natural language processing.</p>
     */ Domain ? : string ; /**
     * <p>The machine learning task your model package accomplishes. Common machine
     *     learning tasks include object detection and image classification.</p>
     */ Task ? : string ; /**
     * <p>The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path must point
     *     to a single gzip compressed tar archive (.tar.gz suffix).</p>
     */ SamplePayloadUrl ? : string ; /**
     * <p>An array of additional Inference Specification objects. Each additional
     *     Inference Specification specifies artifacts based on this model package that can
     *     be used on inference endpoints. Generally used with SageMaker Neo to store the
     *     compiled artifacts. </p>
     */ AdditionalInferenceSpecifications ? : AdditionalInferenceSpecificationDefinition [ ] ; } export namespace CreateModelPackageInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelPackageOutput { /**
     * <p>The Amazon Resource Name (ARN) of the new model package.</p>
     */ ModelPackageArn : string | undefined ; } export namespace CreateModelPackageOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelPackageGroupInput { /**
     * <p>The name of the model group.</p>
     */ ModelPackageGroupName : string | undefined ; /**
     * <p>A description for the model group.</p>
     */ ModelPackageGroupDescription ? : string ; /**
     * <p>A list of key value pairs associated with the model group. For more information, see
     *             <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services
     *                 resources</a> in the <i>Amazon Web Services General Reference Guide</i>.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateModelPackageGroupInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelPackageGroupOutput { /**
     * <p>The Amazon Resource Name (ARN) of the model group.</p>
     */ ModelPackageGroupArn : string | undefined ; } export namespace CreateModelPackageGroupOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum MonitoringProblemType { BINARY_CLASSIFICATION = "BinaryClassification" , MULTICLASS_CLASSIFICATION = "MulticlassClassification" , REGRESSION = "Regression" } /**
 * <p>Container image configuration object for the monitoring job.</p>
 */ export interface ModelQualityAppSpecification { /**
     * <p>The address of the container image that the monitoring job runs.</p>
     */ ImageUri : string | undefined ; /**
     * <p>Specifies the entrypoint for a container that the monitoring job runs.</p>
     */ ContainerEntrypoint ? : string [ ] ; /**
     * <p>An array of arguments for the container used to run the monitoring job.</p>
     */ ContainerArguments ? : string [ ] ; /**
     * <p>An Amazon S3 URI to a script that is called per row prior to running analysis. It can
     *          base64 decode the payload and convert it into a flatted json so that the built-in container
     *          can use the converted data. Applicable only for the built-in (first party)
     *          containers.</p>
     */ RecordPreprocessorSourceUri ? : string ; /**
     * <p>An Amazon S3 URI to a script that is called after analysis has been performed.
     *          Applicable only for the built-in (first party) containers.</p>
     */ PostAnalyticsProcessorSourceUri ? : string ; /**
     * <p>The machine learning problem type of the model that the monitoring job monitors.</p>
     */ ProblemType ? : MonitoringProblemType | string ; /**
     * <p>Sets the environment variables in the container that the monitoring job runs.</p>
     */ Environment ? : { [ key ] : string ; } ; } export namespace ModelQualityAppSpecification { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration for monitoring constraints and monitoring statistics. These baseline
 *          resources are compared against the results of the current job from the series of jobs
 *          scheduled to collect data periodically.</p>
 */ export interface ModelQualityBaselineConfig { /**
     * <p>The name of the job that performs baselining for the monitoring job.</p>
     */ BaseliningJobName ? : string ; /**
     * <p>The constraints resource for a monitoring job.</p>
     */ ConstraintsResource ? : MonitoringConstraintsResource ; } export namespace ModelQualityBaselineConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The input for the model quality monitoring job. Currently endponts are supported for
 *          input for model quality monitoring jobs.</p>
 */ export interface ModelQualityJobInput { /**
     * <p>Input object for the endpoint</p>
     */ EndpointInput : EndpointInput | undefined ; /**
     * <p>The ground truth label provided for the model.</p>
     */ GroundTruthS3Input : MonitoringGroundTruthS3Input | undefined ; } export namespace ModelQualityJobInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelQualityJobDefinitionRequest { /**
     * <p>The name of the monitoring job definition.</p>
     */ JobDefinitionName : string | undefined ; /**
     * <p>Specifies the constraints and baselines for the monitoring job.</p>
     */ ModelQualityBaselineConfig ? : ModelQualityBaselineConfig ; /**
     * <p>The container that runs the monitoring job.</p>
     */ ModelQualityAppSpecification : ModelQualityAppSpecification | undefined ; /**
     * <p>A list of the inputs that are monitored. Currently endpoints are supported.</p>
     */ ModelQualityJobInput : ModelQualityJobInput | undefined ; /**
     * <p>The output configuration for monitoring jobs.</p>
     */ ModelQualityJobOutputConfig : MonitoringOutputConfig | undefined ; /**
     * <p>Identifies the resources to deploy for a monitoring job.</p>
     */ JobResources : MonitoringResources | undefined ; /**
     * <p>Specifies the network configuration for the monitoring job.</p>
     */ NetworkConfig ? : MonitoringNetworkConfig ; /**
     * <p>The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to
     *          perform tasks on your behalf.</p>
     */ RoleArn : string | undefined ; /**
     * <p>A time limit for how long the monitoring job is allowed to run before stopping.</p>
     */ StoppingCondition ? : MonitoringStoppingCondition ; /**
     * <p>(Optional) An array of key-value pairs. For more information, see <a href="https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL">Using Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management
     *             User Guide</i>.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateModelQualityJobDefinitionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateModelQualityJobDefinitionResponse { /**
     * <p>The Amazon Resource Name (ARN) of the model quality monitoring job.</p>
     */ JobDefinitionArn : string | undefined ; } export namespace CreateModelQualityJobDefinitionResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration for monitoring constraints and monitoring statistics. These baseline
 *          resources are compared against the results of the current job from the series of jobs
 *          scheduled to collect data periodically.</p>
 */ export interface MonitoringBaselineConfig { /**
     * <p>The name of the job that performs baselining for the monitoring job.</p>
     */ BaseliningJobName ? : string ; /**
     * <p>The baseline constraint file in Amazon S3 that the current monitoring job should
     *          validated against.</p>
     */ ConstraintsResource ? : MonitoringConstraintsResource ; /**
     * <p>The baseline statistics file in Amazon S3 that the current monitoring job should be
     *          validated against.</p>
     */ StatisticsResource ? : MonitoringStatisticsResource ; } export namespace MonitoringBaselineConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Container image configuration object for the monitoring job.</p>
 */ export interface MonitoringAppSpecification { /**
     * <p>The container image to be run by the monitoring job.</p>
     */ ImageUri : string | undefined ; /**
     * <p>Specifies the entrypoint for a container used to run the monitoring job.</p>
     */ ContainerEntrypoint ? : string [ ] ; /**
     * <p>An array of arguments for the container used to run the monitoring job.</p>
     */ ContainerArguments ? : string [ ] ; /**
     * <p>An Amazon S3 URI to a script that is called per row prior to running analysis. It can
     *          base64 decode the payload and convert it into a flatted json so that the built-in container
     *          can use the converted data. Applicable only for the built-in (first party)
     *          containers.</p>
     */ RecordPreprocessorSourceUri ? : string ; /**
     * <p>An Amazon S3 URI to a script that is called after analysis has been performed.
     *          Applicable only for the built-in (first party) containers.</p>
     */ PostAnalyticsProcessorSourceUri ? : string ; } export namespace MonitoringAppSpecification { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The inputs for a monitoring job.</p>
 */ export interface MonitoringInput { /**
     * <p>The endpoint for a monitoring job.</p>
     */ EndpointInput : EndpointInput | undefined ; } export namespace MonitoringInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Networking options for a job, such as network traffic encryption between containers,
 *          whether to allow inbound and outbound network calls to and from containers, and the VPC
 *          subnets and security groups to use for VPC-enabled jobs.</p>
 */ export interface NetworkConfig { /**
     * <p>Whether to encrypt all communications between distributed processing jobs. Choose
     *             <code>True</code> to encrypt communications. Encryption provides greater security for distributed
     *             processing jobs, but the processing might take longer.</p>
     */ EnableInterContainerTrafficEncryption ? : boolean ; /**
     * <p>Whether to allow inbound and outbound network calls to and from the containers used for
     *          the processing job.</p>
     */ EnableNetworkIsolation ? : boolean ; /**
     * <p>Specifies a VPC that your training jobs and hosted models have access to. Control
     *             access to and from your training and model containers by configuring the VPC. For more
     *             information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html">Protect Endpoints by Using an Amazon Virtual Private Cloud</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html">Protect Training Jobs
     *                 by Using an Amazon Virtual Private Cloud</a>. </p>
     */ VpcConfig ? : VpcConfig ; } export namespace NetworkConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Defines the monitoring job.</p>
 */ export interface MonitoringJobDefinition { /**
     * <p>Baseline configuration used to validate that the data conforms to the specified
     *          constraints and statistics</p>
     */ BaselineConfig ? : MonitoringBaselineConfig ; /**
     * <p>The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker
     *          Endpoint.</p>
     */ MonitoringInputs : MonitoringInput [ ] | undefined ; /**
     * <p>The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage
     *          Service (Amazon S3).</p>
     */ MonitoringOutputConfig : MonitoringOutputConfig | undefined ; /**
     * <p>Identifies the resources, ML compute instances, and ML storage volumes to deploy for a
     *          monitoring job. In distributed processing, you specify more than one instance.</p>
     */ MonitoringResources : MonitoringResources | undefined ; /**
     * <p>Configures the monitoring job to run a specified Docker container image.</p>
     */ MonitoringAppSpecification : MonitoringAppSpecification | undefined ; /**
     * <p>Specifies a time limit for how long the monitoring job is allowed to run.</p>
     */ StoppingCondition ? : MonitoringStoppingCondition ; /**
     * <p>Sets the environment variables in the Docker container.</p>
     */ Environment ? : { [ key ] : string ; } ; /**
     * <p>Specifies networking options for an monitoring job.</p>
     */ NetworkConfig ? : NetworkConfig ; /**
     * <p>The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on
     *          your behalf.</p>
     */ RoleArn : string | undefined ; } export namespace MonitoringJobDefinition { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum MonitoringType { DATA_QUALITY = "DataQuality" , MODEL_BIAS = "ModelBias" , MODEL_EXPLAINABILITY = "ModelExplainability" , MODEL_QUALITY = "ModelQuality" } /**
 * <p>Configuration details about the monitoring schedule.</p>
 */ export interface ScheduleConfig { /**
     * <p>A cron expression that describes details about the monitoring schedule.</p>
     *
     *          <p>Currently the only supported cron expressions are:</p>
     *          <ul>
     *             <li>
     *                <p>If you want to set the job to start every hour, please use the following:</p>
     *                <p>
     *                   <code>Hourly: cron(0 * ? * * *)</code>
     *                </p>
     *             </li>
     *             <li>
     *                <p>If you want to start the job daily:</p>
     *                <p>
     *                   <code>cron(0 [00-23] ? * * *)</code>
     *                </p>
     *             </li>
     *          </ul>
     *          <p>For example, the following are valid cron expressions:</p>
     *          <ul>
     *             <li>
     *                <p>Daily at noon UTC: <code>cron(0 12 ? * * *)</code>
     *                </p>
     *             </li>
     *             <li>
     *                <p>Daily at midnight UTC: <code>cron(0 0 ? * * *)</code>
     *                </p>
     *             </li>
     *          </ul>
     *
     *          <p>To support running every 6, 12 hours, the following are also supported:</p>
     *          <p>
     *             <code>cron(0 [00-23]/[01-24] ? * * *)</code>
     *          </p>
     *          <p>For example, the following are valid cron expressions:</p>
     *          <ul>
     *             <li>
     *                <p>Every 12 hours, starting at 5pm UTC: <code>cron(0 17/12 ? * * *)</code>
     *                </p>
     *             </li>
     *             <li>
     *                <p>Every two hours starting at midnight: <code>cron(0 0/2 ? * * *)</code>
     *                </p>
     *             </li>
     *          </ul>
     *
     *          <note>
     *             <ul>
     *                <li>
     *                   <p>Even though the cron expression is set to start at 5PM UTC, note that there
     *                   could be a delay of 0-20 minutes from the actual requested time to run the
     *                   execution. </p>
     *                </li>
     *                <li>
     *                   <p>We recommend that if you would like a daily schedule, you do not provide this
     *                   parameter. Amazon SageMaker will pick a time for running every day.</p>
     *                </li>
     *             </ul>
     *          </note>
     */ ScheduleExpression : string | undefined ; } export namespace ScheduleConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configures the monitoring schedule and defines the monitoring job.</p>
 */ export interface MonitoringScheduleConfig { /**
     * <p>Configures the monitoring schedule.</p>
     */ ScheduleConfig ? : ScheduleConfig ; /**
     * <p>Defines the monitoring job.</p>
     */ MonitoringJobDefinition ? : MonitoringJobDefinition ; /**
     * <p>The name of the monitoring job definition to schedule.</p>
     */ MonitoringJobDefinitionName ? : string ; /**
     * <p>The type of the monitoring job definition to schedule.</p>
     */ MonitoringType ? : MonitoringType | string ; } export namespace MonitoringScheduleConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateMonitoringScheduleRequest { /**
     * <p>The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within
     *          an Amazon Web Services account.</p>
     */ MonitoringScheduleName : string | undefined ; /**
     * <p>The configuration object that specifies the monitoring schedule and defines the
     *          monitoring job.</p>
     */ MonitoringScheduleConfig : MonitoringScheduleConfig | undefined ; /**
     * <p>(Optional) An array of key-value pairs. For more information, see <a href=" https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL">Using Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management
     *             User Guide</i>.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateMonitoringScheduleRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateMonitoringScheduleResponse { /**
     * <p>The Amazon Resource Name (ARN) of the monitoring schedule.</p>
     */ MonitoringScheduleArn : string | undefined ; } export namespace CreateMonitoringScheduleResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum NotebookInstanceAcceleratorType { ML_EIA1_LARGE = "ml.eia1.large" , ML_EIA1_MEDIUM = "ml.eia1.medium" , ML_EIA1_XLARGE = "ml.eia1.xlarge" , ML_EIA2_LARGE = "ml.eia2.large" , ML_EIA2_MEDIUM = "ml.eia2.medium" , ML_EIA2_XLARGE = "ml.eia2.xlarge" } export enum DirectInternetAccess { DISABLED = "Disabled" , ENABLED = "Enabled" } export enum _InstanceType { ML_C4_2XLARGE = "ml.c4.2xlarge" , ML_C4_4XLARGE = "ml.c4.4xlarge" , ML_C4_8XLARGE = "ml.c4.8xlarge" , ML_C4_XLARGE = "ml.c4.xlarge" , ML_C5D_18XLARGE = "ml.c5d.18xlarge" , ML_C5D_2XLARGE = "ml.c5d.2xlarge" , ML_C5D_4XLARGE = "ml.c5d.4xlarge" , ML_C5D_9XLARGE = "ml.c5d.9xlarge" , ML_C5D_XLARGE = "ml.c5d.xlarge" , ML_C5_18XLARGE = "ml.c5.18xlarge" , ML_C5_2XLARGE = "ml.c5.2xlarge" , ML_C5_4XLARGE = "ml.c5.4xlarge" , ML_C5_9XLARGE = "ml.c5.9xlarge" , ML_C5_XLARGE = "ml.c5.xlarge" , ML_G4DN_12XLARGE = "ml.g4dn.12xlarge" , ML_G4DN_16XLARGE = "ml.g4dn.16xlarge" , ML_G4DN_2XLARGE = "ml.g4dn.2xlarge" , ML_G4DN_4XLARGE = "ml.g4dn.4xlarge" , ML_G4DN_8XLARGE = "ml.g4dn.8xlarge" , ML_G4DN_XLARGE = "ml.g4dn.xlarge" , ML_M4_10XLARGE = "ml.m4.10xlarge" , ML_M4_16XLARGE = "ml.m4.16xlarge" , ML_M4_2XLARGE = "ml.m4.2xlarge" , ML_M4_4XLARGE = "ml.m4.4xlarge" , ML_M4_XLARGE = "ml.m4.xlarge" , ML_M5D_12XLARGE = "ml.m5d.12xlarge" , ML_M5D_16XLARGE = "ml.m5d.16xlarge" , ML_M5D_24XLARGE = "ml.m5d.24xlarge" , ML_M5D_2XLARGE = "ml.m5d.2xlarge" , ML_M5D_4XLARGE = "ml.m5d.4xlarge" , ML_M5D_8XLARGE = "ml.m5d.8xlarge" , ML_M5D_LARGE = "ml.m5d.large" , ML_M5D_XLARGE = "ml.m5d.xlarge" , ML_M5_12XLARGE = "ml.m5.12xlarge" , ML_M5_24XLARGE = "ml.m5.24xlarge" , ML_M5_2XLARGE = "ml.m5.2xlarge" , ML_M5_4XLARGE = "ml.m5.4xlarge" , ML_M5_XLARGE = "ml.m5.xlarge" , ML_P2_16XLARGE = "ml.p2.16xlarge" , ML_P2_8XLARGE = "ml.p2.8xlarge" , ML_P2_XLARGE = "ml.p2.xlarge" , ML_P3DN_24XLARGE = "ml.p3dn.24xlarge" , ML_P3_16XLARGE = "ml.p3.16xlarge" , ML_P3_2XLARGE = "ml.p3.2xlarge" , ML_P3_8XLARGE = "ml.p3.8xlarge" , ML_R5_12XLARGE = "ml.r5.12xlarge" , ML_R5_16XLARGE = "ml.r5.16xlarge" , ML_R5_24XLARGE = "ml.r5.24xlarge" , ML_R5_2XLARGE = "ml.r5.2xlarge" , ML_R5_4XLARGE = "ml.r5.4xlarge" , ML_R5_8XLARGE = "ml.r5.8xlarge" , ML_R5_LARGE = "ml.r5.large" , ML_R5_XLARGE = "ml.r5.xlarge" , ML_T2_2XLARGE = "ml.t2.2xlarge" , ML_T2_LARGE = "ml.t2.large" , ML_T2_MEDIUM = "ml.t2.medium" , ML_T2_XLARGE = "ml.t2.xlarge" , ML_T3_2XLARGE = "ml.t3.2xlarge" , ML_T3_LARGE = "ml.t3.large" , ML_T3_MEDIUM = "ml.t3.medium" , ML_T3_XLARGE = "ml.t3.xlarge" } export enum RootAccess { DISABLED = "Disabled" , ENABLED = "Enabled" } export interface CreateNotebookInstanceInput { /**
     * <p>The name of the new notebook instance.</p>
     */ NotebookInstanceName : string | undefined ; /**
     * <p>The type of ML compute instance to launch for the notebook instance.</p>
     */ InstanceType : _InstanceType | string | undefined ; /**
     * <p>The ID of the subnet in a VPC to which you would like to have a connectivity from
     *             your ML compute instance. </p>
     */ SubnetId ? : string ; /**
     * <p>The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be
     *             for the same VPC as specified in the subnet. </p>
     */ SecurityGroupIds ? : string [ ] ; /**
     * <p> When you send any requests to Amazon Web Services resources from the notebook instance, Amazon SageMaker
     *             assumes this role to perform tasks on your behalf. You must grant this role necessary
     *             permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service
     *             principal (sagemaker.amazonaws.com) permissions to assume this role. For more
     *             information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">Amazon SageMaker Roles</a>. </p>
     *         <note>
     *             <p>To be able to pass this role to Amazon SageMaker, the caller of this API must have the
     *                     <code>iam:PassRole</code> permission.</p>
     *         </note>
     */ RoleArn : string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on
     *             the storage volume attached to your notebook instance. The KMS key you provide must be
     *             enabled. For information, see <a href="https://docs.aws.amazon.com/kms/latest/developerguide/enabling-keys.html">Enabling and Disabling
     *                 Keys</a> in the <i>Amazon Web Services Key Management Service Developer Guide</i>.</p>
     */ KmsKeyId ? : string ; /**
     * <p>An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in
     *             different ways, for example, by purpose, owner, or environment. For more information,
     *             see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services
     *                 Resources</a>.</p>
     */ Tags ? : Tag [ ] ; /**
     * <p>The name of a lifecycle configuration to associate with the notebook instance. For
     *             information about lifestyle configurations, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html">Step 2.1: (Optional)
     *                 Customize a Notebook Instance</a>.</p>
     */ LifecycleConfigName ? : string ; /**
     * <p>Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this
     *             to <code>Disabled</code> this notebook instance is able to access resources only in your
     *             VPC, and is not be able to connect to Amazon SageMaker training and endpoint services unless you
     *             configure a NAT Gateway in your VPC.</p>
     *         <p>For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/appendix-additional-considerations.html#appendix-notebook-and-internet-access">Notebook Instances Are Internet-Enabled by Default</a>. You can set the value
     *             of this parameter to <code>Disabled</code> only if you set a value for the
     *                 <code>SubnetId</code> parameter.</p>
     */ DirectInternetAccess ? : DirectInternetAccess | string ; /**
     * <p>The size, in GB, of the ML storage volume to attach to the notebook instance. The
     *             default value is 5 GB.</p>
     */ VolumeSizeInGB ? : number ; /**
     * <p>A list of Elastic Inference (EI) instance types to associate with this notebook
     *             instance. Currently, only one instance type can be associated with a notebook instance.
     *             For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html">Using Elastic Inference in Amazon SageMaker</a>.</p>
     */ AcceleratorTypes ? : ( NotebookInstanceAcceleratorType | string ) [ ] ; /**
     * <p>A Git repository to associate with the notebook instance as its default code
     *             repository. This can be either the name of a Git repository stored as a resource in your
     *             account, or the URL of a Git repository in <a href="https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html">Amazon Web Services CodeCommit</a> or in any
     *             other Git repository. When you open a notebook instance, it opens in the directory that
     *             contains this repository. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html">Associating Git Repositories with Amazon SageMaker
     *                 Notebook Instances</a>.</p>
     */ DefaultCodeRepository ? : string ; /**
     * <p>An array of up to three Git repositories to associate with the notebook instance.
     *             These can be either the names of Git repositories stored as resources in your account,
     *             or the URL of Git repositories in <a href="https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html">Amazon Web Services CodeCommit</a> or in any
     *             other Git repository. These repositories are cloned at the same level as the default
     *             repository of your notebook instance. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html">Associating Git
     *                 Repositories with Amazon SageMaker Notebook Instances</a>.</p>
     */ AdditionalCodeRepositories ? : string [ ] ; /**
     * <p>Whether root access is enabled or disabled for users of the notebook instance. The
     *             default value is <code>Enabled</code>.</p>
     *         <note>
     *             <p>Lifecycle configurations need root access to be able to set up a notebook
     *                 instance. Because of this, lifecycle configurations associated with a notebook
     *                 instance always run with root access even if you disable root access for
     *                 users.</p>
     *         </note>
     */ RootAccess ? : RootAccess | string ; /**
     * <p>The platform identifier of the notebook instance runtime environment.</p>
     */ PlatformIdentifier ? : string ; } export namespace CreateNotebookInstanceInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateNotebookInstanceOutput { /**
     * <p>The Amazon Resource Name (ARN) of the notebook instance. </p>
     */ NotebookInstanceArn ? : string ; } export namespace CreateNotebookInstanceOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Contains the notebook instance lifecycle configuration script.</p>
 *         <p>Each lifecycle configuration script has a limit of 16384 characters.</p>
 *         <p>The value of the <code>$PATH</code> environment variable that is available to both
 *             scripts is <code>/sbin:bin:/usr/sbin:/usr/bin</code>.</p>
 *         <p>View CloudWatch Logs for notebook instance lifecycle configurations in log group
 *                 <code>/aws/sagemaker/NotebookInstances</code> in log stream
 *                 <code>[notebook-instance-name]/[LifecycleConfigHook]</code>.</p>
 *         <p>Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs
 *             for longer than 5 minutes, it fails and the notebook instance is not created or
 *             started.</p>
 *         <p>For information about notebook instance lifestyle configurations, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html">Step
 *                 2.1: (Optional) Customize a Notebook Instance</a>.</p>
 */ export interface NotebookInstanceLifecycleHook { /**
     * <p>A base64-encoded string that contains a shell script for a notebook instance lifecycle
     *             configuration.</p>
     */ Content ? : string ; } export namespace NotebookInstanceLifecycleHook { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateNotebookInstanceLifecycleConfigInput { /**
     * <p>The name of the lifecycle configuration.</p>
     */ NotebookInstanceLifecycleConfigName : string | undefined ; /**
     * <p>A shell script that runs only once, when you create a notebook instance. The shell
     *             script must be a base64-encoded string.</p>
     */ OnCreate ? : NotebookInstanceLifecycleHook [ ] ; /**
     * <p>A shell script that runs every time you start a notebook instance, including when you
     *             create the notebook instance. The shell script must be a base64-encoded string.</p>
     */ OnStart ? : NotebookInstanceLifecycleHook [ ] ; } export namespace CreateNotebookInstanceLifecycleConfigInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateNotebookInstanceLifecycleConfigOutput { /**
     * <p>The Amazon Resource Name (ARN) of the lifecycle configuration.</p>
     */ NotebookInstanceLifecycleConfigArn ? : string ; } export namespace CreateNotebookInstanceLifecycleConfigOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration that controls the parallelism of the pipeline.
 *             By default, the parallelism configuration specified applies to all
 *             executions of the pipeline unless overridden.</p>
 */ export interface ParallelismConfiguration { /**
     * <p>The max number of steps that can be executed in parallel. </p>
     */ MaxParallelExecutionSteps : number | undefined ; } export namespace ParallelismConfiguration { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The location of the pipeline definition stored in Amazon S3.</p>
 */ export interface PipelineDefinitionS3Location { /**
     * <p>Name of the S3 bucket.</p>
     */ Bucket : string | undefined ; /**
     * <p>The object key (or key name) uniquely identifies the
     *             object in an S3 bucket. </p>
     */ ObjectKey : string | undefined ; /**
     * <p>Version Id of the pipeline definition file. If not specified, Amazon SageMaker
     *             will retrieve the latest version.</p>
     */ VersionId ? : string ; } export namespace PipelineDefinitionS3Location { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreatePipelineRequest { /**
     * <p>The name of the pipeline.</p>
     */ PipelineName : string | undefined ; /**
     * <p>The display name of the pipeline.</p>
     */ PipelineDisplayName ? : string ; /**
     * <p>The JSON pipeline definition of the pipeline.</p>
     */ PipelineDefinition ? : string ; /**
     * <p>The location of the pipeline definition stored in Amazon S3. If specified,
     *             SageMaker will retrieve the pipeline definition from this location.</p>
     */ PipelineDefinitionS3Location ? : PipelineDefinitionS3Location ; /**
     * <p>A description of the pipeline.</p>
     */ PipelineDescription ? : string ; /**
     * <p>A unique, case-sensitive identifier that you provide to ensure the idempotency of the
     *          operation. An idempotent operation completes no more than one time.</p>
     */ ClientRequestToken ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.</p>
     */ RoleArn : string | undefined ; /**
     * <p>A list of tags to apply to the created pipeline.</p>
     */ Tags ? : Tag [ ] ; /**
     * <p>This is the configuration that controls the parallelism of the pipeline.
     *             If specified, it applies to all runs of this pipeline by default.</p>
     */ ParallelismConfiguration ? : ParallelismConfiguration ; } export namespace CreatePipelineRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreatePipelineResponse { /**
     * <p>The Amazon Resource Name (ARN) of the created pipeline.</p>
     */ PipelineArn ? : string ; } export namespace CreatePipelineResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreatePresignedDomainUrlRequest { /**
     * <p>The domain ID.</p>
     */ DomainId : string | undefined ; /**
     * <p>The name of the UserProfile to sign-in as.</p>
     */ UserProfileName : string | undefined ; /**
     * <p>The session expiration duration in seconds. This value defaults to 43200.</p>
     */ SessionExpirationDurationInSeconds ? : number ; /**
     * <p>The number of seconds until the pre-signed URL expires. This value defaults to
     *          300.</p>
     */ ExpiresInSeconds ? : number ; } export namespace CreatePresignedDomainUrlRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreatePresignedDomainUrlResponse { /**
     * <p>The presigned URL.</p>
     */ AuthorizedUrl ? : string ; } export namespace CreatePresignedDomainUrlResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreatePresignedNotebookInstanceUrlInput { /**
     * <p>The name of the notebook instance.</p>
     */ NotebookInstanceName : string | undefined ; /**
     * <p>The duration of the session, in seconds. The default is 12 hours.</p>
     */ SessionExpirationDurationInSeconds ? : number ; } export namespace CreatePresignedNotebookInstanceUrlInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreatePresignedNotebookInstanceUrlOutput { /**
     * <p>A JSON object that contains the URL string. </p>
     */ AuthorizedUrl ? : string ; } export namespace CreatePresignedNotebookInstanceUrlOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Associates a SageMaker job as a trial component with an experiment and trial. Specified when
 *       you call the following APIs:</p>
 *          <ul>
 *             <li>
 *                <p>
 *                   <a>CreateProcessingJob</a>
 *                </p>
 *             </li>
 *             <li>
 *                <p>
 *                   <a>CreateTrainingJob</a>
 *                </p>
 *             </li>
 *             <li>
 *                <p>
 *                   <a>CreateTransformJob</a>
 *                </p>
 *             </li>
 *          </ul>
 */ export interface ExperimentConfig { /**
     * <p>The name of an existing experiment to associate the trial component with.</p>
     */ ExperimentName ? : string ; /**
     * <p>The name of an existing trial to associate the trial component with. If not specified, a
     *       new trial is created.</p>
     */ TrialName ? : string ; /**
     * <p>The display name for the trial component. If this key isn't specified, the display name is
     *       the trial component name.</p>
     */ TrialComponentDisplayName ? : string ; } export namespace ExperimentConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum DataDistributionType { FULLYREPLICATED = "FullyReplicated" , SHARDEDBYS3KEY = "ShardedByS3Key" } export enum InputMode { FILE = "File" , PIPE = "Pipe" } export enum RedshiftResultCompressionType { BZIP2 = "BZIP2" , GZIP = "GZIP" , NONE = "None" , SNAPPY = "SNAPPY" , ZSTD = "ZSTD" } export enum RedshiftResultFormat { CSV = "CSV" , PARQUET = "PARQUET" } /**
 * <p>Configuration for Redshift Dataset Definition input.</p>
 */ export interface RedshiftDatasetDefinition { /**
     * <p>The Redshift cluster Identifier.</p>
     */ ClusterId : string | undefined ; /**
     * <p>The name of the Redshift database used in Redshift query execution.</p>
     */ Database : string | undefined ; /**
     * <p>The database user name used in Redshift query execution.</p>
     */ DbUser : string | undefined ; /**
     * <p>The SQL query statements to be executed.</p>
     */ QueryString : string | undefined ; /**
     * <p>The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.</p>
     */ ClusterRoleArn : string | undefined ; /**
     * <p>The location in Amazon S3 where the Redshift query results are stored.</p>
     */ OutputS3Uri : string | undefined ; /**
     * <p>The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a
     *             Redshift execution.</p>
     */ KmsKeyId ? : string ; /**
     * <p>The data storage format for Redshift query results.</p>
     */ OutputFormat : RedshiftResultFormat | string | undefined ; /**
     * <p>The compression used for Redshift query results.</p>
     */ OutputCompression ? : RedshiftResultCompressionType | string ; } export namespace RedshiftDatasetDefinition { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration for Dataset Definition inputs. The Dataset Definition input must specify
 *             exactly one of either <code>AthenaDatasetDefinition</code> or <code>RedshiftDatasetDefinition</code>
 *             types.</p>
 */ export interface DatasetDefinition { /**
     * <p>Configuration for Athena Dataset Definition input.</p>
     */ AthenaDatasetDefinition ? : AthenaDatasetDefinition ; /**
     * <p>Configuration for Redshift Dataset Definition input.</p>
     */ RedshiftDatasetDefinition ? : RedshiftDatasetDefinition ; /**
     * <p>The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a
     *             processing job. <code>LocalPath</code> is an absolute path to the input data. This is a required
     *             parameter when <code>AppManaged</code> is <code>False</code> (default).</p>
     */ LocalPath ? : string ; /**
     * <p>Whether the generated dataset is <code>FullyReplicated</code> or
     *             <code>ShardedByS3Key</code> (default).</p>
     */ DataDistributionType ? : DataDistributionType | string ; /**
     * <p>Whether to use <code>File</code> or <code>Pipe</code> input mode. In <code>File</code> (default) mode,
     *             Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store
     *             (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used
     *             input mode. In <code>Pipe</code> mode, Amazon SageMaker streams input data from the source directly to your
     *             algorithm without using the EBS volume.</p>
     */ InputMode ? : InputMode | string ; } export namespace DatasetDefinition { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum ProcessingS3CompressionType { GZIP = "Gzip" , NONE = "None" } export enum ProcessingS3DataType { MANIFEST_FILE = "ManifestFile" , S3_PREFIX = "S3Prefix" } /**
 * <p>Configuration for downloading input data from Amazon S3 into the processing container.</p>
 */ export interface ProcessingS3Input { /**
     * <p>The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.</p>
     */ S3Uri : string | undefined ; /**
     * <p>The local path in your container where you want Amazon SageMaker to write input data to.
     *             <code>LocalPath</code> is an absolute path to the input data and must begin with
     *             <code>/opt/ml/processing/</code>. <code>LocalPath</code> is a required
     *             parameter when <code>AppManaged</code> is <code>False</code> (default).</p>
     */ LocalPath ? : string ; /**
     * <p>Whether you use an <code>S3Prefix</code> or a <code>ManifestFile</code> for
     *             the data type. If you choose <code>S3Prefix</code>, <code>S3Uri</code> identifies a key
     *             name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing
     *             job. If you choose <code>ManifestFile</code>, <code>S3Uri</code> identifies an object
     *             that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for
     *             the processing job.</p>
     */ S3DataType : ProcessingS3DataType | string | undefined ; /**
     * <p>Whether to use <code>File</code> or <code>Pipe</code> input mode. In File mode, Amazon SageMaker copies the data
     *             from the input source onto the local ML storage volume before starting your processing
     *             container. This is the most commonly used input mode. In <code>Pipe</code> mode, Amazon SageMaker
     *             streams input data from the source directly to your processing container into named
     *             pipes without using the ML storage volume.</p>
     */ S3InputMode ? : ProcessingS3InputMode | string ; /**
     * <p>Whether to distribute the data from Amazon S3 to all processing instances with
     *             <code>FullyReplicated</code>, or whether the data from Amazon S3 is shared by Amazon S3 key,
     *             downloading one shard of data to each processing instance.</p>
     */ S3DataDistributionType ? : ProcessingS3DataDistributionType | string ; /**
     * <p>Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing
     *             container. <code>Gzip</code> can only be used when <code>Pipe</code> mode is
     *             specified as the <code>S3InputMode</code>. In <code>Pipe</code> mode, Amazon SageMaker streams input
     *             data from the source directly to your container without using the EBS volume.</p>
     */ S3CompressionType ? : ProcessingS3CompressionType | string ; } export namespace ProcessingS3Input { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The inputs for a processing job. The processing input must specify exactly one of either
 *             <code>S3Input</code> or <code>DatasetDefinition</code> types.</p>
 */ export interface ProcessingInput { /**
     * <p>The name for the processing job input.</p>
     */ InputName : string | undefined ; /**
     * <p>When <code>True</code>, input operations such as data download are managed natively by the
     *             processing job application. When <code>False</code> (default), input operations are managed by Amazon SageMaker.</p>
     */ AppManaged ? : boolean ; /**
     * <p>Configuration for downloading input data from Amazon S3 into the processing container.</p>
     */ S3Input ? : ProcessingS3Input ; /**
     * <p>Configuration for a Dataset Definition input. </p>
     */ DatasetDefinition ? : DatasetDefinition ; } export namespace ProcessingInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration for processing job outputs in Amazon SageMaker Feature Store.</p>
 */ export interface ProcessingFeatureStoreOutput { /**
     * <p>The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your
     *             processing script is responsible for putting records into your Feature Store.</p>
     */ FeatureGroupName : string | undefined ; } export namespace ProcessingFeatureStoreOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration for uploading output data to Amazon S3 from the processing container.</p>
 */ export interface ProcessingS3Output { /**
     * <p>A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of
     *             a processing job.</p>
     */ S3Uri : string | undefined ; /**
     * <p>The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3.
     *             <code>LocalPath</code> is an absolute path to a directory containing output files.
     *             This directory will be created by the platform and exist when your container's
     *             entrypoint is invoked.</p>
     */ LocalPath : string | undefined ; /**
     * <p>Whether to upload the results of the processing job continuously or after the job
     *             completes.</p>
     */ S3UploadMode : ProcessingS3UploadMode | string | undefined ; } export namespace ProcessingS3Output { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Describes the results of a processing job. The processing output must specify exactly one of
 *             either <code>S3Output</code> or <code>FeatureStoreOutput</code> types.</p>
 */ export interface ProcessingOutput { /**
     * <p>The name for the processing job output.</p>
     */ OutputName : string | undefined ; /**
     * <p>Configuration for processing job outputs in Amazon S3.</p>
     */ S3Output ? : ProcessingS3Output ; /**
     * <p>Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output
     *             type is only supported when <code>AppManaged</code> is specified. </p>
     */ FeatureStoreOutput ? : ProcessingFeatureStoreOutput ; /**
     * <p>When <code>True</code>, output operations such as data upload are managed natively by the
     *             processing job application. When <code>False</code> (default), output operations are managed by
     *             Amazon SageMaker.</p>
     */ AppManaged ? : boolean ; } export namespace ProcessingOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration for uploading output from the processing container.</p>
 */ export interface ProcessingOutputConfig { /**
     * <p>An array of outputs configuring the data to upload from the processing container.</p>
     */ Outputs : ProcessingOutput [ ] | undefined ; /**
     * <p>The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing
     *             job output. <code>KmsKeyId</code> can be an ID of a KMS key, ARN of a KMS key, alias of
     *             a KMS key, or alias of a KMS key. The <code>KmsKeyId</code> is applied to all
     *             outputs.</p>
     */ KmsKeyId ? : string ; } export namespace ProcessingOutputConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration for the cluster used to run a processing job.</p>
 */ export interface ProcessingClusterConfig { /**
     * <p>The number of ML compute instances to use in the processing job. For distributed
     *             processing jobs, specify a value greater than 1. The default value is 1.</p>
     */ InstanceCount : number | undefined ; /**
     * <p>The ML compute instance type for the processing job.</p>
     */ InstanceType : ProcessingInstanceType | string | undefined ; /**
     * <p>The size of the ML storage volume in gigabytes that you want to provision. You must
     *             specify sufficient ML storage for your scenario.</p>
     *         <note>
     *             <p>Certain Nitro-based instances include local storage with a fixed total size,
     *                 dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts
     *                 the local instance storage instead of Amazon EBS gp2 storage. You can't request a
     *                 <code>VolumeSizeInGB</code> greater than the total size of the local instance
     *                 storage.</p>
     *             <p>For a list of instance types that support local instance storage, including the
     *                 total size per instance type, see <a href="https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes">Instance Store Volumes</a>.</p>
     *         </note>
     */ VolumeSizeInGB : number | undefined ; /**
     * <p>The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the
     *             storage volume attached to the ML compute instance(s) that run the processing job.
     *         </p>
     *         <note>
     *             <p>Certain Nitro-based instances include local storage, dependent on the instance
     *                 type. Local storage volumes are encrypted using a hardware module on the instance.
     *                 You can't request a <code>VolumeKmsKeyId</code> when using an instance type with
     *                 local storage.</p>
     *             <p>For a list of instance types that support local instance storage, see <a href="https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes">Instance Store Volumes</a>.</p>
     *             <p>For more information about local instance storage encryption, see <a href="https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html">SSD
     *                 Instance Store Volumes</a>.</p>
     *         </note>
     */ VolumeKmsKeyId ? : string ; } export namespace ProcessingClusterConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Identifies the resources, ML compute instances, and ML storage volumes to deploy for a
 *             processing job. In distributed training, you specify more than one instance.</p>
 */ export interface ProcessingResources { /**
     * <p>The configuration for the resources in a cluster used to run the processing
     *             job.</p>
     */ ClusterConfig : ProcessingClusterConfig | undefined ; } export namespace ProcessingResources { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configures conditions under which the processing job should be stopped, such as how long
 *             the processing job has been running. After the condition is met, the processing job is stopped.</p>
 */ export interface ProcessingStoppingCondition { /**
     * <p>Specifies the maximum runtime in seconds.</p>
     */ MaxRuntimeInSeconds : number | undefined ; } export namespace ProcessingStoppingCondition { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateProcessingJobRequest { /**
     * <p>An array of inputs configuring the data to download into the
     *             processing container.</p>
     */ ProcessingInputs ? : ProcessingInput [ ] ; /**
     * <p>Output configuration for the processing job.</p>
     */ ProcessingOutputConfig ? : ProcessingOutputConfig ; /**
     * <p> The name of the processing job. The name must be unique within an Amazon Web Services Region in the
     *             Amazon Web Services account.</p>
     */ ProcessingJobName : string | undefined ; /**
     * <p>Identifies the resources, ML compute instances, and ML storage volumes to deploy for a
     *             processing job. In distributed training, you specify more than one instance.</p>
     */ ProcessingResources : ProcessingResources | undefined ; /**
     * <p>The time limit for how long the processing job is allowed to run.</p>
     */ StoppingCondition ? : ProcessingStoppingCondition ; /**
     * <p>Configures the processing job to run a specified Docker container image.</p>
     */ AppSpecification : AppSpecification | undefined ; /**
     * <p>The environment variables to set in the Docker container. Up to
     *             100 key and values entries in the map are supported.</p>
     */ Environment ? : { [ key ] : string ; } ; /**
     * <p>Networking options for a processing job, such as whether to allow inbound and
     *             outbound network calls to and from processing containers, and the VPC subnets and
     *             security groups to use for VPC-enabled processing jobs.</p>
     */ NetworkConfig ? : NetworkConfig ; /**
     * <p>The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on
     *             your behalf.</p>
     */ RoleArn : string | undefined ; /**
     * <p>(Optional) An array of key-value pairs. For more information, see <a href="https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL">Using Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management
     *                 User Guide</i>.</p>
     */ Tags ? : Tag [ ] ; /**
     * <p>Associates a SageMaker job as a trial component with an experiment and trial. Specified when
     *       you call the following APIs:</p>
     *          <ul>
     *             <li>
     *                <p>
     *                   <a>CreateProcessingJob</a>
     *                </p>
     *             </li>
     *             <li>
     *                <p>
     *                   <a>CreateTrainingJob</a>
     *                </p>
     *             </li>
     *             <li>
     *                <p>
     *                   <a>CreateTransformJob</a>
     *                </p>
     *             </li>
     *          </ul>
     */ ExperimentConfig ? : ExperimentConfig ; } export namespace CreateProcessingJobRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateProcessingJobResponse { /**
     * <p>The Amazon Resource Name (ARN) of the processing job.</p>
     */ ProcessingJobArn : string | undefined ; } export namespace CreateProcessingJobResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>A key value pair used when you provision a project as a service catalog product. For
 *             information, see <a href="https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html">What is Amazon Web Services Service
 *                 Catalog</a>.</p>
 */ export interface ProvisioningParameter { /**
     * <p>The key that identifies a provisioning parameter.</p>
     */ Key ? : string ; /**
     * <p>The value of the provisioning parameter.</p>
     */ Value ? : string ; } export namespace ProvisioningParameter { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Details that you specify to provision a service catalog product. For information about
 *             service catalog, see <a href="https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html">What is Amazon Web Services Service
 *                 Catalog</a>.</p>
 */ export interface ServiceCatalogProvisioningDetails { /**
     * <p>The ID of the product to provision.</p>
     */ ProductId : string | undefined ; /**
     * <p>The ID of the provisioning artifact.</p>
     */ ProvisioningArtifactId ? : string ; /**
     * <p>The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path. </p>
     */ PathId ? : string ; /**
     * <p>A list of key value pairs that you specify when you provision a product.</p>
     */ ProvisioningParameters ? : ProvisioningParameter [ ] ; } export namespace ServiceCatalogProvisioningDetails { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateProjectInput { /**
     * <p>The name of the project.</p>
     */ ProjectName : string | undefined ; /**
     * <p>A description for the project.</p>
     */ ProjectDescription ? : string ; /**
     * <p>The product ID and provisioning artifact ID to provision a service catalog. The provisioning
     *             artifact ID will default to the latest provisioning artifact ID of the product, if you don't
     *             provide the provisioning artifact ID. For more information, see <a href="https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html">What is Amazon Web Services Service
     *                 Catalog</a>.</p>
     */ ServiceCatalogProvisioningDetails : ServiceCatalogProvisioningDetails | undefined ; /**
     * <p>An array of key-value pairs that you want to use to organize and track your Amazon Web Services
     *             resource costs. For more information, see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services resources</a> in the <i>Amazon Web Services General Reference Guide</i>.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateProjectInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateProjectOutput { /**
     * <p>The Amazon Resource Name (ARN) of the project.</p>
     */ ProjectArn : string | undefined ; /**
     * <p>The ID of the new project.</p>
     */ ProjectId : string | undefined ; } export namespace CreateProjectOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum StudioLifecycleConfigAppType { JupyterServer = "JupyterServer" , KernelGateway = "KernelGateway" } export interface CreateStudioLifecycleConfigRequest { /**
     * <p>The name of the Studio Lifecycle Configuration to create.</p>
     */ StudioLifecycleConfigName : string | undefined ; /**
     * <p>The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.</p>
     */ StudioLifecycleConfigContent : string | undefined ; /**
     * <p>The App type that the Lifecycle Configuration is attached to.</p>
     */ StudioLifecycleConfigAppType : StudioLifecycleConfigAppType | string | undefined ; /**
     * <p>Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API. </p>
     */ Tags ? : Tag [ ] ; } export namespace CreateStudioLifecycleConfigRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateStudioLifecycleConfigResponse { /**
     * <p>The ARN of your created Lifecycle Configuration.</p>
     */ StudioLifecycleConfigArn ? : string ; } export namespace CreateStudioLifecycleConfigResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration information for the Debugger hook parameters, metric and tensor collections, and
 *             storage paths. To learn more about
 *             how to configure the <code>DebugHookConfig</code> parameter,
 *             see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html">Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job</a>.</p>
 */ export interface DebugHookConfig { /**
     * <p>Path to local storage location for metrics and tensors. Defaults to
     *                 <code>/opt/ml/output/tensors/</code>.</p>
     */ LocalPath ? : string ; /**
     * <p>Path to Amazon S3 storage location for metrics and tensors.</p>
     */ S3OutputPath : string | undefined ; /**
     * <p>Configuration information for the Debugger hook parameters.</p>
     */ HookParameters ? : { [ key ] : string ; } ; /**
     * <p>Configuration information for Debugger tensor collections. To learn more about
     *             how to configure the <code>CollectionConfiguration</code> parameter,
     *             see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html">Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job</a>.
     *         </p>
     */ CollectionConfigurations ? : CollectionConfiguration [ ] ; } export namespace DebugHookConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration information for SageMaker Debugger rules for debugging. To learn more about
 *             how to configure the <code>DebugRuleConfiguration</code> parameter,
 *             see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html">Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job</a>.</p>
 */ export interface DebugRuleConfiguration { /**
     * <p>The name of the rule configuration. It must be unique relative to other rule
     *             configuration names.</p>
     */ RuleConfigurationName : string | undefined ; /**
     * <p>Path to local storage location for output of rules. Defaults to
     *                 <code>/opt/ml/processing/output/rule/</code>.</p>
     */ LocalPath ? : string ; /**
     * <p>Path to Amazon S3 storage location for rules.</p>
     */ S3OutputPath ? : string ; /**
     * <p>The Amazon Elastic Container (ECR) Image for the managed rule evaluation.</p>
     */ RuleEvaluatorImage : string | undefined ; /**
     * <p>The instance type to deploy a Debugger custom rule for debugging a training job.</p>
     */ InstanceType ? : ProcessingInstanceType | string ; /**
     * <p>The size, in GB, of the ML storage volume attached to the processing instance.</p>
     */ VolumeSizeInGB ? : number ; /**
     * <p>Runtime configuration for rule container.</p>
     */ RuleParameters ? : { [ key ] : string ; } ; } export namespace DebugRuleConfiguration { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration information for Debugger system monitoring, framework profiling, and
 *             storage paths.</p>
 */ export interface ProfilerConfig { /**
     * <p>Path to Amazon S3 storage location for system and framework metrics.</p>
     */ S3OutputPath : string | undefined ; /**
     * <p>A time interval for capturing system metrics in milliseconds. Available values are
     *             100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.</p>
     */ ProfilingIntervalInMilliseconds ? : number ; /**
     * <p>Configuration information for capturing framework metrics. Available key strings for different profiling options are
     *             <code>DetailedProfilingConfig</code>, <code>PythonProfilingConfig</code>, and <code>DataLoaderProfilingConfig</code>.
     *             The following codes are configuration structures for the <code>ProfilingParameters</code> parameter. To learn more about
     *             how to configure the <code>ProfilingParameters</code> parameter,
     *             see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html">Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job</a>.
     *         </p>
     */ ProfilingParameters ? : { [ key ] : string ; } ; } export namespace ProfilerConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration information for profiling rules.</p>
 */ export interface ProfilerRuleConfiguration { /**
     * <p>The name of the rule configuration. It must be unique relative to other rule configuration names.</p>
     */ RuleConfigurationName : string | undefined ; /**
     * <p>Path to local storage location for output of rules. Defaults to <code>/opt/ml/processing/output/rule/</code>. </p>
     */ LocalPath ? : string ; /**
     * <p>Path to Amazon S3 storage location for rules.</p>
     */ S3OutputPath ? : string ; /**
     * <p>The Amazon Elastic Container (ECR) Image for the managed rule evaluation.</p>
     */ RuleEvaluatorImage : string | undefined ; /**
     * <p>The instance type to deploy a Debugger custom rule for profiling a training job.</p>
     */ InstanceType ? : ProcessingInstanceType | string ; /**
     * <p>The size, in GB, of the ML storage volume attached to the processing instance.</p>
     */ VolumeSizeInGB ? : number ; /**
     * <p>Runtime configuration for rule container.</p>
     */ RuleParameters ? : { [ key ] : string ; } ; } export namespace ProfilerRuleConfiguration { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configuration of storage locations for the Debugger TensorBoard output data.</p>
 */ export interface TensorBoardOutputConfig { /**
     * <p>Path to local storage location for tensorBoard output. Defaults to
     *                 <code>/opt/ml/output/tensorboard</code>.</p>
     */ LocalPath ? : string ; /**
     * <p>Path to Amazon S3 storage location for TensorBoard output.</p>
     */ S3OutputPath : string | undefined ; } export namespace TensorBoardOutputConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateTrainingJobRequest { /**
     * <p>The name of the training job. The name must be unique within an Amazon Web Services Region in an
     *             Amazon Web Services account. </p>
     */ TrainingJobName : string | undefined ; /**
     * <p>Algorithm-specific parameters that influence the quality of the model. You set
     *             hyperparameters before you start the learning process. For a list of hyperparameters for
     *             each training algorithm provided by Amazon SageMaker, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html">Algorithms</a>. </p>
     *         <p>You can specify a maximum of 100 hyperparameters. Each hyperparameter is a
     *             key-value pair. Each key and value is limited to 256 characters, as specified by the
     *                 <code>Length Constraint</code>. </p>
     */ HyperParameters ? : { [ key ] : string ; } ; /**
     * <p>The registry path of the Docker image that contains the training algorithm and
     *             algorithm-specific metadata, including the input mode. For more information about
     *             algorithms provided by Amazon SageMaker, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html">Algorithms</a>. For information about
     *             providing your own algorithms, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html">Using Your Own Algorithms with Amazon
     *                 SageMaker</a>. </p>
     */ AlgorithmSpecification : AlgorithmSpecification | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform
     *             tasks on your behalf. </p>
     *         <p>During model training, Amazon SageMaker needs your permission to read input data from an S3
     *             bucket, download a Docker image that contains training code, write model artifacts to an
     *             S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant
     *             permissions for all of these tasks to an IAM role. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">Amazon SageMaker
     *                 Roles</a>. </p>
     *         <note>
     *             <p>To be able to pass this role to Amazon SageMaker, the caller of this API must have the
     *                     <code>iam:PassRole</code> permission.</p>
     *         </note>
     */ RoleArn : string | undefined ; /**
     * <p>An array of <code>Channel</code> objects. Each channel is a named input source.
     *                 <code>InputDataConfig</code>
     *             describes the input data and its location. </p>
     *         <p>Algorithms can accept input data from one or more channels. For example, an
     *             algorithm might have two channels of input data, <code>training_data</code> and
     *                 <code>validation_data</code>. The configuration for each channel provides the S3,
     *             EFS, or FSx location where the input data is stored. It also provides information about
     *             the stored data: the MIME type, compression method, and whether the data is wrapped in
     *             RecordIO format. </p>
     *         <p>Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input
     *             data files from an S3 bucket to a local directory in the Docker container, or makes it
     *             available as input streams. For example, if you specify an EFS location, input data
     *             files will be made available as input streams. They do not need to be
     *             downloaded.</p>
     */ InputDataConfig ? : Channel [ ] ; /**
     * <p>Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker
     *             creates subfolders for the artifacts. </p>
     */ OutputDataConfig : OutputDataConfig | undefined ; /**
     * <p>The resources, including the ML compute instances and ML storage volumes, to use
     *             for model training. </p>
     *         <p>ML storage volumes store model artifacts and incremental states. Training
     *             algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use
     *             the ML storage volume to store the training data, choose <code>File</code> as the
     *                 <code>TrainingInputMode</code> in the algorithm specification. For distributed
     *             training algorithms, specify an instance count greater than 1.</p>
     */ ResourceConfig : ResourceConfig | undefined ; /**
     * <p>A <a>VpcConfig</a> object that specifies the VPC that you want your
     *             training job to connect to. Control access to and from your training container by
     *             configuring the VPC. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html">Protect Training Jobs by Using an Amazon
     *                 Virtual Private Cloud</a>.</p>
     */ VpcConfig ? : VpcConfig ; /**
     * <p>Specifies a limit to how long a model training job can run. It also specifies how long
     *             a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker
     *             ends the training job. Use this API to cap model training costs.</p>
     *         <p>To stop a job, Amazon SageMaker sends the algorithm the <code>SIGTERM</code> signal, which delays
     *             job termination for 120 seconds. Algorithms can use this 120-second window to save the
     *             model artifacts, so the results of training are not lost. </p>
     */ StoppingCondition : StoppingCondition | undefined ; /**
     * <p>An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in
     *             different ways, for example, by purpose, owner, or environment. For more information,
     *             see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services
     *                 Resources</a>.</p>
     */ Tags ? : Tag [ ] ; /**
     * <p>Isolates the training container. No inbound or outbound network calls can be made,
     *             except for calls between peers within a training cluster for distributed training. If
     *             you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker
     *             downloads and uploads customer data and model artifacts through the specified VPC, but
     *             the training container does not have network access.</p>
     */ EnableNetworkIsolation ? : boolean ; /**
     * <p>To encrypt all communications between ML compute instances in distributed training,
     *             choose <code>True</code>. Encryption provides greater security for distributed training,
     *             but training might take longer. How long it takes depends on the amount of communication
     *             between compute instances, especially if you use a deep learning algorithm in
     *             distributed training. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/train-encrypt.html">Protect Communications Between ML
     *                 Compute Instances in a Distributed Training Job</a>.</p>
     */ EnableInterContainerTrafficEncryption ? : boolean ; /**
     * <p>To train models using managed spot training, choose <code>True</code>. Managed spot
     *             training provides a fully managed and scalable infrastructure for training machine
     *             learning models. this option is useful when training jobs can be interrupted and when
     *             there is flexibility when the training job is run. </p>
     *         <p>The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be
     *             used as a starting point to train models incrementally. Amazon SageMaker provides metrics and
     *             logs in CloudWatch. They can be used to see when managed spot training jobs are running,
     *             interrupted, resumed, or completed. </p>
     */ EnableManagedSpotTraining ? : boolean ; /**
     * <p>Contains information about the output location for managed spot training checkpoint
     *             data.</p>
     */ CheckpointConfig ? : CheckpointConfig ; /**
     * <p>Configuration information for the Debugger hook parameters, metric and tensor collections, and
     *             storage paths. To learn more about
     *             how to configure the <code>DebugHookConfig</code> parameter,
     *             see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html">Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job</a>.</p>
     */ DebugHookConfig ? : DebugHookConfig ; /**
     * <p>Configuration information for Debugger rules for debugging output tensors.</p>
     */ DebugRuleConfigurations ? : DebugRuleConfiguration [ ] ; /**
     * <p>Configuration of storage locations for the Debugger TensorBoard output data.</p>
     */ TensorBoardOutputConfig ? : TensorBoardOutputConfig ; /**
     * <p>Associates a SageMaker job as a trial component with an experiment and trial. Specified when
     *       you call the following APIs:</p>
     *          <ul>
     *             <li>
     *                <p>
     *                   <a>CreateProcessingJob</a>
     *                </p>
     *             </li>
     *             <li>
     *                <p>
     *                   <a>CreateTrainingJob</a>
     *                </p>
     *             </li>
     *             <li>
     *                <p>
     *                   <a>CreateTransformJob</a>
     *                </p>
     *             </li>
     *          </ul>
     */ ExperimentConfig ? : ExperimentConfig ; /**
     * <p>Configuration information for Debugger system monitoring, framework profiling, and
     *             storage paths.</p>
     */ ProfilerConfig ? : ProfilerConfig ; /**
     * <p>Configuration information for Debugger rules for profiling system and framework
     *             metrics.</p>
     */ ProfilerRuleConfigurations ? : ProfilerRuleConfiguration [ ] ; /**
     * <p>The environment variables to set in the Docker container.</p>
     */ Environment ? : { [ key ] : string ; } ; /**
     * <p>The number of times to retry the job when the job fails due to an
     *                 <code>InternalServerError</code>.</p>
     */ RetryStrategy ? : RetryStrategy ; } export namespace CreateTrainingJobRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateTrainingJobResponse { /**
     * <p>The Amazon Resource Name (ARN) of the training job.</p>
     */ TrainingJobArn : string | undefined ; } export namespace CreateTrainingJobResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum JoinSource { INPUT = "Input" , NONE = "None" } /**
 * <p>The data structure used to specify the data to be used for inference in a batch
 *             transform job and to associate the data that is relevant to the prediction results in
 *             the output. The input filter provided allows you to exclude input data that is not
 *             needed for inference in a batch transform job. The output filter provided allows you to
 *             include input data relevant to interpreting the predictions in the output from the job.
 *             For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html">Associate Prediction
 *                 Results with their Corresponding Input Records</a>.</p>
 */ export interface DataProcessing { /**
     * <p>A <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#data-processing-operators">JSONPath</a> expression used to select a portion of the input data to pass to
     *             the algorithm. Use the <code>InputFilter</code> parameter to exclude fields, such as an
     *             ID column, from the input. If you want Amazon SageMaker to pass the entire input dataset to the
     *             algorithm, accept the default value <code>$</code>.</p>
     *         <p>Examples: <code>"$"</code>, <code>"$[1:]"</code>, <code>"$.features"</code>
     *          </p>
     */ InputFilter ? : string ; /**
     * <p>A <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#data-processing-operators">JSONPath</a> expression used to select a portion of the joined dataset to save
     *             in the output file for a batch transform job. If you want Amazon SageMaker to store the entire input
     *             dataset in the output file, leave the default value, <code>$</code>. If you specify
     *             indexes that aren't within the dimension size of the joined dataset, you get an
     *             error.</p>
     *         <p>Examples: <code>"$"</code>, <code>"$[0,5:]"</code>,
     *                 <code>"$['id','SageMakerOutput']"</code>
     *          </p>
     */ OutputFilter ? : string ; /**
     * <p>Specifies the source of the data to join with the transformed data. The valid values
     *             are <code>None</code> and <code>Input</code>. The default value is <code>None</code>,
     *             which specifies not to join the input with the transformed data. If you want the batch
     *             transform job to join the original input data with the transformed data, set
     *                 <code>JoinSource</code> to <code>Input</code>. You can specify
     *                 <code>OutputFilter</code> as an additional filter to select a portion of the joined
     *             dataset and store it in the output file.</p>
     *         <p>For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to
     *             the input JSON object in an attribute called <code>SageMakerOutput</code>. The joined
     *             result for JSON must be a key-value pair object. If the input is not a key-value pair
     *             object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored
     *             under the <code>SageMakerInput</code> key and the results are stored in
     *                 <code>SageMakerOutput</code>.</p>
     *         <p>For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with
     *             the input by appending each transformed row to the end of the input. The joined data has
     *             the original input data followed by the transformed data and the output is a CSV
     *             file.</p>
     *         <p>For information on how joining in applied, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#batch-transform-data-processing-workflow">Workflow for Associating Inferences with Input Records</a>.</p>
     */ JoinSource ? : JoinSource | string ; } export namespace DataProcessing { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configures the timeout and maximum number of retries for processing a transform job
 *             invocation.</p>
 */ export interface ModelClientConfig { /**
     * <p>The timeout value in seconds for an invocation request.</p>
     */ InvocationsTimeoutInSeconds ? : number ; /**
     * <p>The maximum number of retries when invocation requests are failing.</p>
     */ InvocationsMaxRetries ? : number ; } export namespace ModelClientConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateTransformJobRequest { /**
     * <p>The name of the transform job. The name must be unique within an Amazon Web Services Region in an
     *             Amazon Web Services account. </p>
     */ TransformJobName : string | undefined ; /**
     * <p>The name of the model that you want to use for the transform job.
     *             <code>ModelName</code> must be the name of an existing Amazon SageMaker model within an Amazon Web Services
     *             Region in an Amazon Web Services account.</p>
     */ ModelName : string | undefined ; /**
     * <p>The maximum number of parallel requests that can be sent to each instance in a
     *             transform job. If <code>MaxConcurrentTransforms</code> is set to <code>0</code> or left
     *             unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your
     *             chosen algorithm. If the execution-parameters endpoint is not enabled, the default value
     *             is <code>1</code>. For more information on execution-parameters, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-batch-code.html#your-algorithms-batch-code-how-containe-serves-requests">How Containers Serve Requests</a>. For built-in algorithms, you don't need to
     *             set a value for <code>MaxConcurrentTransforms</code>.</p>
     */ MaxConcurrentTransforms ? : number ; /**
     * <p>Configures the timeout and maximum number of retries for processing a transform job
     *             invocation.</p>
     */ ModelClientConfig ? : ModelClientConfig ; /**
     * <p>The maximum allowed size of the payload, in MB. A <i>payload</i> is the
     *             data portion of a record (without metadata). The value in <code>MaxPayloadInMB</code>
     *             must be greater than, or equal to, the size of a single record. To estimate the size of
     *             a record in MB, divide the size of your dataset by the number of records. To ensure that
     *             the records fit within the maximum payload size, we recommend using a slightly larger
     *             value. The default value is <code>6</code> MB.
     *             </p>
     *         <p>For cases where the payload might be arbitrarily large and is transmitted using HTTP
     *             chunked encoding, set the value to <code>0</code>.
     *             This
     *             feature works only in supported algorithms. Currently, Amazon SageMaker built-in
     *             algorithms do not support HTTP chunked encoding.</p>
     */ MaxPayloadInMB ? : number ; /**
     * <p>Specifies the number of records to include in a mini-batch for an HTTP inference
     *             request. A <i>record</i>
     *             <i></i> is a single unit of input data that
     *             inference can be made on. For example, a single line in a CSV file is a record. </p>
     *         <p>To enable the batch strategy, you must set the <code>SplitType</code> property to
     *                 <code>Line</code>, <code>RecordIO</code>, or <code>TFRecord</code>.</p>
     *         <p>To use only one record when making an HTTP invocation request to a container, set
     *                 <code>BatchStrategy</code> to <code>SingleRecord</code> and <code>SplitType</code>
     *             to <code>Line</code>.</p>
     *         <p>To fit as many records in a mini-batch as can fit within the
     *                 <code>MaxPayloadInMB</code> limit, set <code>BatchStrategy</code> to
     *                 <code>MultiRecord</code> and <code>SplitType</code> to <code>Line</code>.</p>
     */ BatchStrategy ? : BatchStrategy | string ; /**
     * <p>The environment variables to set in the Docker container. We support up to 16 key and
     *             values entries in the map.</p>
     */ Environment ? : { [ key ] : string ; } ; /**
     * <p>Describes the input source and
     *             the
     *             way the transform job consumes it.</p>
     */ TransformInput : TransformInput | undefined ; /**
     * <p>Describes the results of the transform job.</p>
     */ TransformOutput : TransformOutput | undefined ; /**
     * <p>Describes the resources, including
     *             ML
     *             instance types and ML instance count, to use for the transform
     *             job.</p>
     */ TransformResources : TransformResources | undefined ; /**
     * <p>The data structure used to specify the data to be used for inference in a batch
     *             transform job and to associate the data that is relevant to the prediction results in
     *             the output. The input filter provided allows you to exclude input data that is not
     *             needed for inference in a batch transform job. The output filter provided allows you to
     *             include input data relevant to interpreting the predictions in the output from the job.
     *             For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html">Associate Prediction
     *                 Results with their Corresponding Input Records</a>.</p>
     */ DataProcessing ? : DataProcessing ; /**
     * <p>(Optional)
     *             An
     *             array of key-value pairs. For more information, see <a href="https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what">Using
     *                 Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management User
     *                 Guide</i>.</p>
     */ Tags ? : Tag [ ] ; /**
     * <p>Associates a SageMaker job as a trial component with an experiment and trial. Specified when
     *       you call the following APIs:</p>
     *          <ul>
     *             <li>
     *                <p>
     *                   <a>CreateProcessingJob</a>
     *                </p>
     *             </li>
     *             <li>
     *                <p>
     *                   <a>CreateTrainingJob</a>
     *                </p>
     *             </li>
     *             <li>
     *                <p>
     *                   <a>CreateTransformJob</a>
     *                </p>
     *             </li>
     *          </ul>
     */ ExperimentConfig ? : ExperimentConfig ; } export namespace CreateTransformJobRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateTransformJobResponse { /**
     * <p>The Amazon Resource Name (ARN) of the transform job.</p>
     */ TransformJobArn : string | undefined ; } export namespace CreateTransformJobResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateTrialRequest { /**
     * <p>The name of the trial. The name must be unique in your Amazon Web Services account and is not
     *       case-sensitive.</p>
     */ TrialName : string | undefined ; /**
     * <p>The name of the trial as displayed. The name doesn't need to be unique. If
     *         <code>DisplayName</code> isn't specified, <code>TrialName</code> is displayed.</p>
     */ DisplayName ? : string ; /**
     * <p>The name of the experiment to associate the trial with.</p>
     */ ExperimentName : string | undefined ; /**
     * <p>Metadata properties of the tracking entity, trial, or trial component.</p>
     */ MetadataProperties ? : MetadataProperties ; /**
     * <p>A list of tags to associate with the trial. You can use <a>Search</a> API to
     *       search on the tags.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateTrialRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateTrialResponse { /**
     * <p>The Amazon Resource Name (ARN) of the trial.</p>
     */ TrialArn ? : string ; } export namespace CreateTrialResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Represents an input or output artifact of a trial component. You specify
 *         <code>TrialComponentArtifact</code> as part of the <code>InputArtifacts</code> and
 *         <code>OutputArtifacts</code> parameters in the <a>CreateTrialComponent</a>
 *       request.</p>
 *          <p>Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and
 *       instance types. Examples of output artifacts are metrics, snapshots, logs, and images.</p>
 */ export interface TrialComponentArtifact { /**
     * <p>The media type of the artifact, which indicates the type of data in the artifact file. The
     *       media type consists of a <i>type</i> and a <i>subtype</i>
     *       concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The
     *       type specifies the category of the media. The subtype specifies the kind of data.</p>
     */ MediaType ? : string ; /**
     * <p>The location of the artifact.</p>
     */ Value : string | undefined ; } export namespace TrialComponentArtifact { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The value of a hyperparameter. Only one of <code>NumberValue</code> or
 *         <code>StringValue</code> can be specified.</p>
 *          <p>This object is specified in the <a>CreateTrialComponent</a> request.</p>
 */ export type TrialComponentParameterValue = TrialComponentParameterValue . NumberValueMember | TrialComponentParameterValue . StringValueMember | TrialComponentParameterValue . $UnknownMember ; export namespace TrialComponentParameterValue { /**
     * <p>The string value of a categorical hyperparameter. If you specify a value for this
     *       parameter, you can't specify the <code>NumberValue</code> parameter.</p>
     */ export interface StringValueMember { StringValue : string ; NumberValue ? : never ; $unknown ? : never ; } /**
     * <p>The numeric value of a numeric hyperparameter. If you specify a value for this parameter,
     *       you can't specify the <code>StringValue</code> parameter.</p>
     */ export interface NumberValueMember { StringValue ? : never ; NumberValue : number ; $unknown ? : never ; } export interface $UnknownMember { StringValue ? : never ; NumberValue ? : never ; $unknown : [ string , any ] ; } export interface Visitor < T > { StringValue : ( value : string ) => T ; NumberValue : ( value : number ) => T ; _ : ( name : string , value : any ) => T ; } export const visit = < T > ( value , visitor ) : T => { if ( value . StringValue !== undefined ) return visitor . StringValue ( value . StringValue ) ; if ( value . NumberValue !== undefined ) return visitor . NumberValue ( value . NumberValue ) ; return visitor . _ ( value . $unknown [ 0 ] , value . $unknown [ 1 ] ) ; } ; internal export const filterSensitiveLog = ( obj ) : any => { if ( obj . StringValue !== undefined ) return { StringValue : obj . StringValue } ; if ( obj . NumberValue !== undefined ) return { NumberValue : obj . NumberValue } ; if ( obj . $unknown !== undefined ) return { [ obj . $unknown [ 0 ] ] : "UNKNOWN" } ; } ; } export enum TrialComponentPrimaryStatus { COMPLETED = "Completed" , FAILED = "Failed" , IN_PROGRESS = "InProgress" , STOPPED = "Stopped" , STOPPING = "Stopping" } /**
 * <p>The status of the trial component.</p>
 */ export interface TrialComponentStatus { /**
     * <p>The status of the trial component.</p>
     */ PrimaryStatus ? : TrialComponentPrimaryStatus | string ; /**
     * <p>If the component failed, a message describing why.</p>
     */ Message ? : string ; } export namespace TrialComponentStatus { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateTrialComponentRequest { /**
     * <p>The name of the component. The name must be unique in your Amazon Web Services account and is not
     *       case-sensitive.</p>
     */ TrialComponentName : string | undefined ; /**
     * <p>The name of the component as displayed. The name doesn't need to be unique. If
     *         <code>DisplayName</code> isn't specified, <code>TrialComponentName</code> is
     *       displayed.</p>
     */ DisplayName ? : string ; /**
     * <p>The status of the component. States include:</p>
     *          <ul>
     *             <li>
     *                <p>InProgress</p>
     *             </li>
     *             <li>
     *                <p>Completed</p>
     *             </li>
     *             <li>
     *                <p>Failed</p>
     *             </li>
     *          </ul>
     */ Status ? : TrialComponentStatus ; /**
     * <p>When the component started.</p>
     */ StartTime ? : Date ; /**
     * <p>When the component ended.</p>
     */ EndTime ? : Date ; /**
     * <p>The hyperparameters for the component.</p>
     */ Parameters ? : { [ key ] : TrialComponentParameterValue ; } ; /**
     * <p>The input artifacts for the component. Examples of input artifacts are datasets,
     *       algorithms, hyperparameters, source code, and instance types.</p>
     */ InputArtifacts ? : { [ key ] : TrialComponentArtifact ; } ; /**
     * <p>The output artifacts for the component. Examples of output artifacts are metrics,
     *       snapshots, logs, and images.</p>
     */ OutputArtifacts ? : { [ key ] : TrialComponentArtifact ; } ; /**
     * <p>Metadata properties of the tracking entity, trial, or trial component.</p>
     */ MetadataProperties ? : MetadataProperties ; /**
     * <p>A list of tags to associate with the component. You can use <a>Search</a> API
     *       to search on the tags.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateTrialComponentRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , ... ( obj . Parameters && { Parameters : Object . entries ( obj . Parameters ) . reduce ( ( acc , [ key , value ] ) => ( { ... acc , [ key ] : TrialComponentParameterValue . filterSensitiveLog ( value ) , } ) , { } ) , } ) , } ) ; } export interface CreateTrialComponentResponse { /**
     * <p>The Amazon Resource Name (ARN) of the trial component.</p>
     */ TrialComponentArn ? : string ; } export namespace CreateTrialComponentResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateUserProfileRequest { /**
     * <p>The ID of the associated Domain.</p>
     */ DomainId : string | undefined ; /**
     * <p>A name for the UserProfile. This value is not case sensitive.</p>
     */ UserProfileName : string | undefined ; /**
     * <p>A specifier for the type of value specified in SingleSignOnUserValue.  Currently, the only supported value is "UserName".
     *            If the Domain's AuthMode is SSO, this field is required.  If the Domain's AuthMode is not SSO, this field cannot be specified.
     *        </p>
     */ SingleSignOnUserIdentifier ? : string ; /**
     * <p>The username of the associated Amazon Web Services Single Sign-On User for this UserProfile.  If the Domain's AuthMode is SSO, this field is
     *            required, and must match a valid username of a user in your directory.  If the Domain's AuthMode is not SSO, this field cannot be specified.
     *        </p>
     */ SingleSignOnUserValue ? : string ; /**
     * <p>Each tag consists of a key and an optional value.
     *          Tag keys must be unique per resource.</p>
     *          <p>Tags that you specify for the User Profile are also added to all Apps that the
     *           User Profile launches.</p>
     */ Tags ? : Tag [ ] ; /**
     * <p>A collection of settings.</p>
     */ UserSettings ? : UserSettings ; } export namespace CreateUserProfileRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateUserProfileResponse { /**
     * <p>The user profile Amazon Resource Name (ARN).</p>
     */ UserProfileArn ? : string ; } export namespace CreateUserProfileResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Use this parameter to configure your OIDC Identity Provider (IdP).</p>
 */ export interface OidcConfig { /**
     * <p>The OIDC IdP client ID used to configure your private workforce.</p>
     */ ClientId : string | undefined ; /**
     * <p>The OIDC IdP client secret used to configure your private workforce.</p>
     */ ClientSecret : string | undefined ; /**
     * <p>The OIDC IdP issuer used to configure your private workforce.</p>
     */ Issuer : string | undefined ; /**
     * <p>The OIDC IdP authorization endpoint used to configure your private workforce.</p>
     */ AuthorizationEndpoint : string | undefined ; /**
     * <p>The OIDC IdP token endpoint used to configure your private workforce.</p>
     */ TokenEndpoint : string | undefined ; /**
     * <p>The OIDC IdP user information endpoint used to configure your private workforce.</p>
     */ UserInfoEndpoint : string | undefined ; /**
     * <p>The OIDC IdP logout endpoint used to configure your private workforce.</p>
     */ LogoutEndpoint : string | undefined ; /**
     * <p>The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.</p>
     */ JwksUri : string | undefined ; } export namespace OidcConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , ... ( obj . ClientSecret && { ClientSecret : SENSITIVE_STRING } ) , } ) ; } /**
 * <p>A list of IP address ranges (<a href="https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html">CIDRs</a>). Used to create an allow
 *             list of IP addresses for a private workforce. Workers will only be able to login to their worker portal from an
 *             IP address within this range. By default, a workforce isn't restricted to specific IP addresses.</p>
 */ export interface SourceIpConfig { /**
     * <p>A list of one to ten <a href="https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html">Classless Inter-Domain Routing</a> (CIDR) values.</p>
     *         <p>Maximum: Ten CIDR values</p>
     *         <note>
     *             <p>The following Length Constraints apply to individual CIDR values in
     *                 the CIDR value list.</p>
     *         </note>
     */ Cidrs : string [ ] | undefined ; } export namespace SourceIpConfig { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateWorkforceRequest { /**
     * <p>Use this parameter to configure an Amazon Cognito private workforce.
     *       A single Cognito workforce is created using and corresponds to a single
     *       <a href="https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html">
     *       Amazon Cognito user pool</a>.</p>
     *
     *          <p>Do not use <code>OidcConfig</code> if you specify values for
     *       <code>CognitoConfig</code>.</p>
     */ CognitoConfig ? : CognitoConfig ; /**
     * <p>Use this parameter to configure a private workforce using your own OIDC Identity Provider.</p>
     *          <p>Do not use <code>CognitoConfig</code> if you specify values for
     *       <code>OidcConfig</code>.</p>
     */ OidcConfig ? : OidcConfig ; /**
     * <p>A list of IP address ranges (<a href="https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html">CIDRs</a>). Used to create an allow
     *             list of IP addresses for a private workforce. Workers will only be able to login to their worker portal from an
     *             IP address within this range. By default, a workforce isn't restricted to specific IP addresses.</p>
     */ SourceIpConfig ? : SourceIpConfig ; /**
     * <p>The name of the private workforce.</p>
     */ WorkforceName : string | undefined ; /**
     * <p>An array of key-value pairs that contain metadata to help you categorize and
     *       organize our workforce. Each tag consists of a key and a value,
     *       both of which you define.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateWorkforceRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , ... ( obj . OidcConfig && { OidcConfig : OidcConfig . filterSensitiveLog ( obj . OidcConfig ) } ) , } ) ; } export interface CreateWorkforceResponse { /**
     * <p>The Amazon Resource Name (ARN) of the workforce.</p>
     */ WorkforceArn : string | undefined ; } export namespace CreateWorkforceResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>A list of user groups that exist in your OIDC Identity Provider (IdP).
 *             One to ten groups can be used to create a single private work team.
 *             When you add a user group to the list of <code>Groups</code>, you can add that user group to one or more
 *             private work teams. If you add a user group to a private work team, all workers in that user group
 *             are added to the work team.</p>
 */ export interface OidcMemberDefinition { /**
     * <p>A list of comma seperated strings that identifies
     *             user groups in your OIDC IdP. Each user group is
     *             made up of a group of private workers.</p>
     */ Groups : string [ ] | undefined ; } export namespace OidcMemberDefinition { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.</p>
 */ export interface MemberDefinition { /**
     * <p>The Amazon Cognito user group that is part of the work team.</p>
     */ CognitoMemberDefinition ? : CognitoMemberDefinition ; /**
     * <p>A list user groups that exist in your OIDC Identity Provider (IdP).
     *             One to ten groups can be used to create a single private work team.
     *             When you add a user group to the list of <code>Groups</code>, you can add that user group to one or more
     *             private work teams. If you add a user group to a private work team, all workers in that user group
     *             are added to the work team.</p>
     */ OidcMemberDefinition ? : OidcMemberDefinition ; } export namespace MemberDefinition { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Configures Amazon SNS notifications of available or expiring work items for work
 *             teams.</p>
 */ export interface NotificationConfiguration { /**
     * <p>The ARN for the Amazon SNS topic to which notifications should be published.</p>
     */ NotificationTopicArn ? : string ; } export namespace NotificationConfiguration { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateWorkteamRequest { /**
     * <p>The name of the work team. Use this name to identify the work team.</p>
     */ WorkteamName : string | undefined ; /**
     * <p>The name of the workforce.</p>
     */ WorkforceName ? : string ; /**
     * <p>A list of <code>MemberDefinition</code> objects that contains objects that identify
     *             the workers that make up the work team. </p>
     *         <p>Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For
     *             private workforces created using Amazon Cognito use <code>CognitoMemberDefinition</code>. For
     *             workforces created using your own OIDC identity provider (IdP) use
     *                 <code>OidcMemberDefinition</code>. Do not provide input for both of these parameters
     *             in a single request.</p>
     *         <p>For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito
     *                 <i>user groups</i> within the user pool used to create a workforce. All of the
     *                 <code>CognitoMemberDefinition</code> objects that make up the member definition must
     *             have the same <code>ClientId</code> and <code>UserPool</code> values. To add a Amazon
     *             Cognito user group to an existing worker pool, see <a href="">Adding groups to a User
     *                 Pool</a>. For more information about user pools, see <a href="https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html">Amazon Cognito User
     *                 Pools</a>.</p>
     *         <p>For workforces created using your own OIDC IdP, specify the user groups that you want to
     *         include in your private work team in <code>OidcMemberDefinition</code> by listing those groups
     *         in <code>Groups</code>.</p>
     */ MemberDefinitions : MemberDefinition [ ] | undefined ; /**
     * <p>A description of the work team.</p>
     */ Description : string | undefined ; /**
     * <p>Configures notification of workers regarding available or expiring work items.</p>
     */ NotificationConfiguration ? : NotificationConfiguration ; /**
     * <p>An array of key-value pairs.</p>
     *         <p>For more information, see <a href="https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-resource-tags.html">Resource
     *                 Tag</a> and <a href="https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what">Using
     *                     Cost Allocation Tags</a> in the <i> Amazon Web Services Billing and Cost Management User
     *                 Guide</i>.</p>
     */ Tags ? : Tag [ ] ; } export namespace CreateWorkteamRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface CreateWorkteamResponse { /**
     * <p>The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the
     *             work team.</p>
     */ WorkteamArn ? : string ; } export namespace CreateWorkteamResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p></p>
 */ export interface DataCaptureConfigSummary { /**
     * <p></p>
     */ EnableCapture : boolean | undefined ; /**
     * <p></p>
     */ CaptureStatus : CaptureStatus | string | undefined ; /**
     * <p></p>
     */ CurrentSamplingPercentage : number | undefined ; /**
     * <p></p>
     */ DestinationS3Uri : string | undefined ; /**
     * <p></p>
     */ KmsKeyId : string | undefined ; } export namespace DataCaptureConfigSummary { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum RuleEvaluationStatus { ERROR = "Error" , IN_PROGRESS = "InProgress" , ISSUES_FOUND = "IssuesFound" , NO_ISSUES_FOUND = "NoIssuesFound" , STOPPED = "Stopped" , STOPPING = "Stopping" } /**
 * <p>Information about the status of the rule evaluation.</p>
 */ export interface DebugRuleEvaluationStatus { /**
     * <p>The name of the rule configuration.</p>
     */ RuleConfigurationName ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of the rule evaluation job.</p>
     */ RuleEvaluationJobArn ? : string ; /**
     * <p>Status of the rule evaluation.</p>
     */ RuleEvaluationStatus ? : RuleEvaluationStatus | string ; /**
     * <p>Details from the rule evaluation.</p>
     */ StatusDetails ? : string ; /**
     * <p>Timestamp when the rule evaluation status was last modified.</p>
     */ LastModifiedTime ? : Date ; } export namespace DebugRuleEvaluationStatus { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteActionRequest { /**
     * <p>The name of the action to delete.</p>
     */ ActionName : string | undefined ; } export namespace DeleteActionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteActionResponse { /**
     * <p>The Amazon Resource Name (ARN) of the action.</p>
     */ ActionArn ? : string ; } export namespace DeleteActionResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteAlgorithmInput { /**
     * <p>The name of the algorithm to delete.</p>
     */ AlgorithmName : string | undefined ; } export namespace DeleteAlgorithmInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteAppRequest { /**
     * <p>The domain ID.</p>
     */ DomainId : string | undefined ; /**
     * <p>The user profile name.</p>
     */ UserProfileName : string | undefined ; /**
     * <p>The type of app.</p>
     */ AppType : AppType | string | undefined ; /**
     * <p>The name of the app.</p>
     */ AppName : string | undefined ; } export namespace DeleteAppRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteAppImageConfigRequest { /**
     * <p>The name of the AppImageConfig to delete.</p>
     */ AppImageConfigName : string | undefined ; } export namespace DeleteAppImageConfigRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteArtifactRequest { /**
     * <p>The Amazon Resource Name (ARN) of the artifact to delete.</p>
     */ ArtifactArn ? : string ; /**
     * <p>The URI of the source.</p>
     */ Source ? : ArtifactSource ; } export namespace DeleteArtifactRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteArtifactResponse { /**
     * <p>The Amazon Resource Name (ARN) of the artifact.</p>
     */ ArtifactArn ? : string ; } export namespace DeleteArtifactResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteAssociationRequest { /**
     * <p>The ARN of the source.</p>
     */ SourceArn : string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the destination.</p>
     */ DestinationArn : string | undefined ; } export namespace DeleteAssociationRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteAssociationResponse { /**
     * <p>The ARN of the source.</p>
     */ SourceArn ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of the destination.</p>
     */ DestinationArn ? : string ; } export namespace DeleteAssociationResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteCodeRepositoryInput { /**
     * <p>The name of the Git repository to delete.</p>
     */ CodeRepositoryName : string | undefined ; } export namespace DeleteCodeRepositoryInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteContextRequest { /**
     * <p>The name of the context to delete.</p>
     */ ContextName : string | undefined ; } export namespace DeleteContextRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteContextResponse { /**
     * <p>The Amazon Resource Name (ARN) of the context.</p>
     */ ContextArn ? : string ; } export namespace DeleteContextResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteDataQualityJobDefinitionRequest { /**
     * <p>The name of the data quality monitoring job definition to delete.</p>
     */ JobDefinitionName : string | undefined ; } export namespace DeleteDataQualityJobDefinitionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteDeviceFleetRequest { /**
     * <p>The name of the fleet to delete.</p>
     */ DeviceFleetName : string | undefined ; } export namespace DeleteDeviceFleetRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum RetentionType { Delete = "Delete" , Retain = "Retain" } /**
 * <p>The retention policy for data stored on an Amazon Elastic File System (EFS) volume.</p>
 */ export interface RetentionPolicy { /**
     * <p>The default is <code>Retain</code>, which specifies to keep the data stored on the EFS volume.</p>
     *          <p>Specify <code>Delete</code> to delete the data stored on the EFS volume.</p>
     */ HomeEfsFileSystem ? : RetentionType | string ; } export namespace RetentionPolicy { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteDomainRequest { /**
     * <p>The domain ID.</p>
     */ DomainId : string | undefined ; /**
     * <p>The retention policy for this domain, which specifies whether resources will be retained after the Domain is deleted.
     *            By default, all resources are retained (not automatically deleted).
     *        </p>
     */ RetentionPolicy ? : RetentionPolicy ; } export namespace DeleteDomainRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteEndpointInput { /**
     * <p>The name of the endpoint that you want to delete.</p>
     */ EndpointName : string | undefined ; } export namespace DeleteEndpointInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteEndpointConfigInput { /**
     * <p>The name of the endpoint configuration that you want to delete.</p>
     */ EndpointConfigName : string | undefined ; } export namespace DeleteEndpointConfigInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteExperimentRequest { /**
     * <p>The name of the experiment to delete.</p>
     */ ExperimentName : string | undefined ; } export namespace DeleteExperimentRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteExperimentResponse { /**
     * <p>The Amazon Resource Name (ARN) of the experiment that is being deleted.</p>
     */ ExperimentArn ? : string ; } export namespace DeleteExperimentResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteFeatureGroupRequest { /**
     * <p>The name of the <code>FeatureGroup</code> you want to delete. The name must be unique
     *          within an Amazon Web Services Region in an Amazon Web Services account. </p>
     */ FeatureGroupName : string | undefined ; } export namespace DeleteFeatureGroupRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteFlowDefinitionRequest { /**
     * <p>The name of the flow definition you are deleting.</p>
     */ FlowDefinitionName : string | undefined ; } export namespace DeleteFlowDefinitionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteFlowDefinitionResponse { } export namespace DeleteFlowDefinitionResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteHumanTaskUiRequest { /**
     * <p>The name of the human task user interface (work task template) you want to delete.</p>
     */ HumanTaskUiName : string | undefined ; } export namespace DeleteHumanTaskUiRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteHumanTaskUiResponse { } export namespace DeleteHumanTaskUiResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteImageRequest { /**
     * <p>The name of the image to delete.</p>
     */ ImageName : string | undefined ; } export namespace DeleteImageRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteImageResponse { } export namespace DeleteImageResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteImageVersionRequest { /**
     * <p>The name of the image.</p>
     */ ImageName : string | undefined ; /**
     * <p>The version to delete.</p>
     */ Version : number | undefined ; } export namespace DeleteImageVersionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteImageVersionResponse { } export namespace DeleteImageVersionResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteModelInput { /**
     * <p>The name of the model to delete.</p>
     */ ModelName : string | undefined ; } export namespace DeleteModelInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteModelBiasJobDefinitionRequest { /**
     * <p>The name of the model bias job definition to delete.</p>
     */ JobDefinitionName : string | undefined ; } export namespace DeleteModelBiasJobDefinitionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteModelExplainabilityJobDefinitionRequest { /**
     * <p>The name of the model explainability job definition to delete.</p>
     */ JobDefinitionName : string | undefined ; } export namespace DeleteModelExplainabilityJobDefinitionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteModelPackageInput { /**
     * <p>The name or Amazon Resource Name (ARN) of the model package to delete.</p>
     *         <p>When you specify a name, the name must have 1 to 63 characters. Valid
     *           characters are a-z, A-Z, 0-9, and - (hyphen).</p>
     */ ModelPackageName : string | undefined ; } export namespace DeleteModelPackageInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteModelPackageGroupInput { /**
     * <p>The name of the model group to delete.</p>
     */ ModelPackageGroupName : string | undefined ; } export namespace DeleteModelPackageGroupInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteModelPackageGroupPolicyInput { /**
     * <p>The name of the model group for which to delete the policy.</p>
     */ ModelPackageGroupName : string | undefined ; } export namespace DeleteModelPackageGroupPolicyInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteModelQualityJobDefinitionRequest { /**
     * <p>The name of the model quality monitoring job definition to delete.</p>
     */ JobDefinitionName : string | undefined ; } export namespace DeleteModelQualityJobDefinitionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteMonitoringScheduleRequest { /**
     * <p>The name of the monitoring schedule to delete.</p>
     */ MonitoringScheduleName : string | undefined ; } export namespace DeleteMonitoringScheduleRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteNotebookInstanceInput { /**
     * <p>The name of the Amazon SageMaker notebook instance to delete.</p>
     */ NotebookInstanceName : string | undefined ; } export namespace DeleteNotebookInstanceInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteNotebookInstanceLifecycleConfigInput { /**
     * <p>The name of the lifecycle configuration to delete.</p>
     */ NotebookInstanceLifecycleConfigName : string | undefined ; } export namespace DeleteNotebookInstanceLifecycleConfigInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeletePipelineRequest { /**
     * <p>The name of the pipeline to delete.</p>
     */ PipelineName : string | undefined ; /**
     * <p>A unique, case-sensitive identifier that you provide to ensure the idempotency of the
     *          operation. An idempotent operation completes no more than one time.</p>
     */ ClientRequestToken ? : string ; } export namespace DeletePipelineRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeletePipelineResponse { /**
     * <p>The Amazon Resource Name (ARN) of the pipeline to delete.</p>
     */ PipelineArn ? : string ; } export namespace DeletePipelineResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteProjectInput { /**
     * <p>The name of the project to delete.</p>
     */ ProjectName : string | undefined ; } export namespace DeleteProjectInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteStudioLifecycleConfigRequest { /**
     * <p>The name of the Studio Lifecycle Configuration to delete.</p>
     */ StudioLifecycleConfigName : string | undefined ; } export namespace DeleteStudioLifecycleConfigRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteTagsInput { /**
     * <p>The Amazon Resource Name (ARN) of the resource whose tags you want to
     *             delete.</p>
     */ ResourceArn : string | undefined ; /**
     * <p>An array or one or more tag keys to delete.</p>
     */ TagKeys : string [ ] | undefined ; } export namespace DeleteTagsInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteTagsOutput { } export namespace DeleteTagsOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteTrialRequest { /**
     * <p>The name of the trial to delete.</p>
     */ TrialName : string | undefined ; } export namespace DeleteTrialRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteTrialResponse { /**
     * <p>The Amazon Resource Name (ARN) of the trial that is being deleted.</p>
     */ TrialArn ? : string ; } export namespace DeleteTrialResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteTrialComponentRequest { /**
     * <p>The name of the component to delete.</p>
     */ TrialComponentName : string | undefined ; } export namespace DeleteTrialComponentRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteTrialComponentResponse { /**
     * <p>The Amazon Resource Name (ARN) of the component is being deleted.</p>
     */ TrialComponentArn ? : string ; } export namespace DeleteTrialComponentResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteUserProfileRequest { /**
     * <p>The domain ID.</p>
     */ DomainId : string | undefined ; /**
     * <p>The user profile name.</p>
     */ UserProfileName : string | undefined ; } export namespace DeleteUserProfileRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteWorkforceRequest { /**
     * <p>The name of the workforce.</p>
     */ WorkforceName : string | undefined ; } export namespace DeleteWorkforceRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteWorkforceResponse { } export namespace DeleteWorkforceResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteWorkteamRequest { /**
     * <p>The name of the work team to delete.</p>
     */ WorkteamName : string | undefined ; } export namespace DeleteWorkteamRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeleteWorkteamResponse { /**
     * <p>Returns <code>true</code> if the work team was successfully deleted; otherwise,
     *             returns <code>false</code>.</p>
     */ Success : boolean | undefined ; } export namespace DeleteWorkteamResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this <a>ProductionVariant</a>.</p>
 *         <p>If you used the <code>registry/repository[:tag]</code> form to specify the image path
 *             of the primary container when you created the model hosted in this
 *                 <code>ProductionVariant</code>, the path resolves to a path of the form
 *                 <code>registry/repository[@digest]</code>. A digest is a hash value that identifies
 *             a specific version of an image. For information about Amazon ECR paths, see <a href="https://docs.aws.amazon.com/AmazonECR/latest/userguide/docker-pull-ecr-image.html">Pulling an Image</a> in the <i>Amazon ECR User Guide</i>.</p>
 */ export interface DeployedImage { /**
     * <p>The image path you specified when you created the model.</p>
     */ SpecifiedImage ? : string ; /**
     * <p>The specific digest path of the image hosted in this
     *             <code>ProductionVariant</code>.</p>
     */ ResolvedImage ? : string ; /**
     * <p>The date and time when the image path for the model resolved to the
     *                 <code>ResolvedImage</code>
     *          </p>
     */ ResolutionTime ? : Date ; } export namespace DeployedImage { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DeregisterDevicesRequest { /**
     * <p>The name of the fleet the devices belong to.</p>
     */ DeviceFleetName : string | undefined ; /**
     * <p>The unique IDs of the devices.</p>
     */ DeviceNames : string [ ] | undefined ; } export namespace DeregisterDevicesRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeActionRequest { /**
     * <p>The name of the action to describe.</p>
     */ ActionName : string | undefined ; } export namespace DescribeActionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeActionResponse { /**
     * <p>The name of the action.</p>
     */ ActionName ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of the action.</p>
     */ ActionArn ? : string ; /**
     * <p>The source of the action.</p>
     */ Source ? : ActionSource ; /**
     * <p>The type of the action.</p>
     */ ActionType ? : string ; /**
     * <p>The description of the action.</p>
     */ Description ? : string ; /**
     * <p>The status of the action.</p>
     */ Status ? : ActionStatus | string ; /**
     * <p>A list of the action's properties.</p>
     */ Properties ? : { [ key ] : string ; } ; /**
     * <p>When the action was created.</p>
     */ CreationTime ? : Date ; /**
     * <p>Information about the user who created or modified an experiment, trial, trial
     *       component, lineage group, or project.</p>
     */ CreatedBy ? : UserContext ; /**
     * <p>When the action was last modified.</p>
     */ LastModifiedTime ? : Date ; /**
     * <p>Information about the user who created or modified an experiment, trial, trial
     *       component, lineage group, or project.</p>
     */ LastModifiedBy ? : UserContext ; /**
     * <p>Metadata properties of the tracking entity, trial, or trial component.</p>
     */ MetadataProperties ? : MetadataProperties ; /**
     * <p>The Amazon Resource Name (ARN) of the lineage group.</p>
     */ LineageGroupArn ? : string ; } export namespace DescribeActionResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeAlgorithmInput { /**
     * <p>The name of the algorithm to describe.</p>
     */ AlgorithmName : string | undefined ; } export namespace DescribeAlgorithmInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeAlgorithmOutput { /**
     * <p>The name of the algorithm being described.</p>
     */ AlgorithmName : string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the algorithm.</p>
     */ AlgorithmArn : string | undefined ; /**
     * <p>A brief summary about the algorithm.</p>
     */ AlgorithmDescription ? : string ; /**
     * <p>A timestamp specifying when the algorithm was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>Details about training jobs run by this algorithm.</p>
     */ TrainingSpecification : TrainingSpecification | undefined ; /**
     * <p>Details about inference jobs that the algorithm runs.</p>
     */ InferenceSpecification ? : InferenceSpecification ; /**
     * <p>Details about configurations for one or more training jobs that Amazon SageMaker runs to test the
     *             algorithm.</p>
     */ ValidationSpecification ? : AlgorithmValidationSpecification ; /**
     * <p>The current status of the algorithm.</p>
     */ AlgorithmStatus : AlgorithmStatus | string | undefined ; /**
     * <p>Details about the current status of the algorithm.</p>
     */ AlgorithmStatusDetails : AlgorithmStatusDetails | undefined ; /**
     * <p>The product identifier of the algorithm.</p>
     */ ProductId ? : string ; /**
     * <p>Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.</p>
     */ CertifyForMarketplace ? : boolean ; } export namespace DescribeAlgorithmOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeAppRequest { /**
     * <p>The domain ID.</p>
     */ DomainId : string | undefined ; /**
     * <p>The user profile name.</p>
     */ UserProfileName : string | undefined ; /**
     * <p>The type of app.</p>
     */ AppType : AppType | string | undefined ; /**
     * <p>The name of the app.</p>
     */ AppName : string | undefined ; } export namespace DescribeAppRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeAppResponse { /**
     * <p>The Amazon Resource Name (ARN) of the app.</p>
     */ AppArn ? : string ; /**
     * <p>The type of app.</p>
     */ AppType ? : AppType | string ; /**
     * <p>The name of the app.</p>
     */ AppName ? : string ; /**
     * <p>The domain ID.</p>
     */ DomainId ? : string ; /**
     * <p>The user profile name.</p>
     */ UserProfileName ? : string ; /**
     * <p>The status.</p>
     */ Status ? : AppStatus | string ; /**
     * <p>The timestamp of the last health check.</p>
     */ LastHealthCheckTimestamp ? : Date ; /**
     * <p>The timestamp of the last user's activity. <code>LastUserActivityTimestamp</code> is also updated when SageMaker performs health checks without user activity. As a result, this value is set to the same value as <code>LastHealthCheckTimestamp</code>.</p>
     */ LastUserActivityTimestamp ? : Date ; /**
     * <p>The creation time.</p>
     */ CreationTime ? : Date ; /**
     * <p>The failure reason.</p>
     */ FailureReason ? : string ; /**
     * <p>The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.</p>
     */ ResourceSpec ? : ResourceSpec ; } export namespace DescribeAppResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeAppImageConfigRequest { /**
     * <p>The name of the AppImageConfig to describe.</p>
     */ AppImageConfigName : string | undefined ; } export namespace DescribeAppImageConfigRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeAppImageConfigResponse { /**
     * <p>The Amazon Resource Name (ARN) of the AppImageConfig.</p>
     */ AppImageConfigArn ? : string ; /**
     * <p>The name of the AppImageConfig.</p>
     */ AppImageConfigName ? : string ; /**
     * <p>When the AppImageConfig was created.</p>
     */ CreationTime ? : Date ; /**
     * <p>When the AppImageConfig was last modified.</p>
     */ LastModifiedTime ? : Date ; /**
     * <p>The configuration of a KernelGateway app.</p>
     */ KernelGatewayImageConfig ? : KernelGatewayImageConfig ; } export namespace DescribeAppImageConfigResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeArtifactRequest { /**
     * <p>The Amazon Resource Name (ARN) of the artifact to describe.</p>
     */ ArtifactArn : string | undefined ; } export namespace DescribeArtifactRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeArtifactResponse { /**
     * <p>The name of the artifact.</p>
     */ ArtifactName ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of the artifact.</p>
     */ ArtifactArn ? : string ; /**
     * <p>The source of the artifact.</p>
     */ Source ? : ArtifactSource ; /**
     * <p>The type of the artifact.</p>
     */ ArtifactType ? : string ; /**
     * <p>A list of the artifact's properties.</p>
     */ Properties ? : { [ key ] : string ; } ; /**
     * <p>When the artifact was created.</p>
     */ CreationTime ? : Date ; /**
     * <p>Information about the user who created or modified an experiment, trial, trial
     *       component, lineage group, or project.</p>
     */ CreatedBy ? : UserContext ; /**
     * <p>When the artifact was last modified.</p>
     */ LastModifiedTime ? : Date ; /**
     * <p>Information about the user who created or modified an experiment, trial, trial
     *       component, lineage group, or project.</p>
     */ LastModifiedBy ? : UserContext ; /**
     * <p>Metadata properties of the tracking entity, trial, or trial component.</p>
     */ MetadataProperties ? : MetadataProperties ; /**
     * <p>The Amazon Resource Name (ARN) of the lineage group.</p>
     */ LineageGroupArn ? : string ; } export namespace DescribeArtifactResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeAutoMLJobRequest { /**
     * <p>Requests information about an AutoML job using its unique name.</p>
     */ AutoMLJobName : string | undefined ; } export namespace DescribeAutoMLJobRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Provides information about the endpoint of the model deployment.</p>
 */ export interface ModelDeployResult { /**
     * <p>The name of the endpoint to which the model has been deployed.</p>
     *          <note>
     *             <p>If model deployment fails, this field is omitted from the response.</p>
     *          </note>
     */ EndpointName ? : string ; } export namespace ModelDeployResult { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The resolved attributes.</p>
 */ export interface ResolvedAttributes { /**
     * <p>Specifies a metric to minimize or maximize as the objective of a job.</p>
     */ AutoMLJobObjective ? : AutoMLJobObjective ; /**
     * <p>The problem type.</p>
     */ ProblemType ? : ProblemType | string ; /**
     * <p>How long a job is allowed to run, or how many candidates a job is allowed to
     *          generate.</p>
     */ CompletionCriteria ? : AutoMLJobCompletionCriteria ; } export namespace ResolvedAttributes { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeAutoMLJobResponse { /**
     * <p>Returns the name of the AutoML job.</p>
     */ AutoMLJobName : string | undefined ; /**
     * <p>Returns the ARN of the AutoML job.</p>
     */ AutoMLJobArn : string | undefined ; /**
     * <p>Returns the input data configuration for the AutoML job..</p>
     */ InputDataConfig : AutoMLChannel [ ] | undefined ; /**
     * <p>Returns the job's output data config.</p>
     */ OutputDataConfig : AutoMLOutputDataConfig | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that
     *          has read permission to the input data location and write permission to the output data
     *          location in Amazon S3.</p>
     */ RoleArn : string | undefined ; /**
     * <p>Returns the job's objective.</p>
     */ AutoMLJobObjective ? : AutoMLJobObjective ; /**
     * <p>Returns the job's problem type.</p>
     */ ProblemType ? : ProblemType | string ; /**
     * <p>Returns the configuration for the AutoML job.</p>
     */ AutoMLJobConfig ? : AutoMLJobConfig ; /**
     * <p>Returns the creation time of the AutoML job.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>Returns the end time of the AutoML job.</p>
     */ EndTime ? : Date ; /**
     * <p>Returns the job's last modified time.</p>
     */ LastModifiedTime : Date | undefined ; /**
     * <p>Returns the failure reason for an AutoML job, when applicable.</p>
     */ FailureReason ? : string ; /**
     * <p>Returns a list of reasons for partial failures within an AutoML job.</p>
     */ PartialFailureReasons ? : AutoMLPartialFailureReason [ ] ; /**
     * <p>Returns the job's best <code>AutoMLCandidate</code>.</p>
     */ BestCandidate ? : AutoMLCandidate ; /**
     * <p>Returns the status of the AutoML job.</p>
     */ AutoMLJobStatus : AutoMLJobStatus | string | undefined ; /**
     * <p>Returns the secondary status of the AutoML job.</p>
     */ AutoMLJobSecondaryStatus : AutoMLJobSecondaryStatus | string | undefined ; /**
     * <p>Indicates whether the output for an AutoML job generates candidate definitions
     *          only.</p>
     */ GenerateCandidateDefinitionsOnly ? : boolean ; /**
     * <p>Returns information on the job's artifacts found in
     *          <code>AutoMLJobArtifacts</code>.</p>
     */ AutoMLJobArtifacts ? : AutoMLJobArtifacts ; /**
     * <p>This contains <code>ProblemType</code>, <code>AutoMLJobObjective</code>, and
     *             <code>CompletionCriteria</code>. If you do not provide these values, they are
     *          auto-inferred. If you do provide them, the values used are the ones you provide.</p>
     */ ResolvedAttributes ? : ResolvedAttributes ; /**
     * <p>Indicates whether the model was deployed automatically to an endpoint and the name of
     *          that endpoint if deployed automatically.</p>
     */ ModelDeployConfig ? : ModelDeployConfig ; /**
     * <p>Provides information about endpoint for the model deployment.</p>
     */ ModelDeployResult ? : ModelDeployResult ; } export namespace DescribeAutoMLJobResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeCodeRepositoryInput { /**
     * <p>The name of the Git repository to describe.</p>
     */ CodeRepositoryName : string | undefined ; } export namespace DescribeCodeRepositoryInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeCodeRepositoryOutput { /**
     * <p>The name of the Git repository.</p>
     */ CodeRepositoryName : string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the Git repository.</p>
     */ CodeRepositoryArn : string | undefined ; /**
     * <p>The date and time that the repository was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>The date and time that the repository was last changed.</p>
     */ LastModifiedTime : Date | undefined ; /**
     * <p>Configuration details about the repository, including the URL where the repository is
     *             located, the default branch, and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets
     *             Manager secret that contains the credentials used to access the repository.</p>
     */ GitConfig ? : GitConfig ; } export namespace DescribeCodeRepositoryOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeCompilationJobRequest { /**
     * <p>The name of the model compilation job that you want information about.</p>
     */ CompilationJobName : string | undefined ; } export namespace DescribeCompilationJobRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Provides information about the location that is configured for storing model
 *             artifacts. </p>
 *         <p>Model artifacts are the output that results from training a model, and typically
 *             consist of trained parameters, a model definition that describes how to compute
 *             inferences, and other metadata.</p>
 */ export interface ModelArtifacts { /**
     * <p>The path of the S3 object that contains the model artifacts. For example,
     *                 <code>s3://bucket-name/keynameprefix/model.tar.gz</code>.</p>
     */ S3ModelArtifacts : string | undefined ; } export namespace ModelArtifacts { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Provides information to verify the integrity of stored model artifacts. </p>
 */ export interface ModelDigests { /**
     * <p>Provides a hash value that uniquely identifies the stored model
     *             artifacts.</p>
     */ ArtifactDigest ? : string ; } export namespace ModelDigests { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeCompilationJobResponse { /**
     * <p>The name of the model compilation job.</p>
     */ CompilationJobName : string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the model compilation job.</p>
     */ CompilationJobArn : string | undefined ; /**
     * <p>The status of the model compilation job.</p>
     */ CompilationJobStatus : CompilationJobStatus | string | undefined ; /**
     * <p>The time when the model compilation job started the <code>CompilationJob</code>
     *             instances. </p>
     *         <p>You are billed for the time between this timestamp and the timestamp in the <a>DescribeCompilationJobResponse$CompilationEndTime</a> field. In Amazon CloudWatch Logs,
     *             the start time might be later than this time. That's because it takes time to download
     *             the compilation job, which depends on the size of the compilation job container. </p>
     */ CompilationStartTime ? : Date ; /**
     * <p>The time when the model compilation job on a compilation job instance ended. For a
     *             successful or stopped job, this is when the job's model artifacts have finished
     *             uploading. For a failed job, this is when Amazon SageMaker detected that the job failed. </p>
     */ CompilationEndTime ? : Date ; /**
     * <p>Specifies a limit to how long a model compilation job can run. When the job reaches
     *             the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training
     *             costs.</p>
     */ StoppingCondition : StoppingCondition | undefined ; /**
     * <p>The inference image to use when compiling a model.
     *             Specify an image only if the target device is a cloud instance.</p>
     */ InferenceImage ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of the versioned model package that was
     *     provided to SageMaker Neo when you initiated a compilation job.</p>
     */ ModelPackageVersionArn ? : string ; /**
     * <p>The time that the model compilation job was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>The time that the status
     *             of
     *             the model compilation job was last modified.</p>
     */ LastModifiedTime : Date | undefined ; /**
     * <p>If a model compilation job failed, the reason it failed. </p>
     */ FailureReason : string | undefined ; /**
     * <p>Information about the location in Amazon S3 that has been configured for storing the model
     *             artifacts used in the compilation job.</p>
     */ ModelArtifacts : ModelArtifacts | undefined ; /**
     * <p>Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.</p>
     */ ModelDigests ? : ModelDigests ; /**
     * <p>The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model
     *             compilation job.</p>
     */ RoleArn : string | undefined ; /**
     * <p>Information about the location in Amazon S3 of the input model artifacts, the name and
     *             shape of the expected data inputs, and the framework in which the model was
     *             trained.</p>
     */ InputConfig : InputConfig | undefined ; /**
     * <p>Information about the output location for the compiled model and the target device
     *             that the model runs on.</p>
     */ OutputConfig : OutputConfig | undefined ; /**
     * <p>A <a>VpcConfig</a> object that specifies the VPC that you want your
     *             compilation job to connect to. Control access to your models by
     *             configuring the VPC. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/neo-vpc.html">Protect Compilation Jobs by Using an Amazon
     *                 Virtual Private Cloud</a>.</p>
     */ VpcConfig ? : NeoVpcConfig ; } export namespace DescribeCompilationJobResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeContextRequest { /**
     * <p>The name of the context to describe.</p>
     */ ContextName : string | undefined ; } export namespace DescribeContextRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeContextResponse { /**
     * <p>The name of the context.</p>
     */ ContextName ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of the context.</p>
     */ ContextArn ? : string ; /**
     * <p>The source of the context.</p>
     */ Source ? : ContextSource ; /**
     * <p>The type of the context.</p>
     */ ContextType ? : string ; /**
     * <p>The description of the context.</p>
     */ Description ? : string ; /**
     * <p>A list of the context's properties.</p>
     */ Properties ? : { [ key ] : string ; } ; /**
     * <p>When the context was created.</p>
     */ CreationTime ? : Date ; /**
     * <p>Information about the user who created or modified an experiment, trial, trial
     *       component, lineage group, or project.</p>
     */ CreatedBy ? : UserContext ; /**
     * <p>When the context was last modified.</p>
     */ LastModifiedTime ? : Date ; /**
     * <p>Information about the user who created or modified an experiment, trial, trial
     *       component, lineage group, or project.</p>
     */ LastModifiedBy ? : UserContext ; /**
     * <p>The Amazon Resource Name (ARN) of the lineage group.</p>
     */ LineageGroupArn ? : string ; } export namespace DescribeContextResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeDataQualityJobDefinitionRequest { /**
     * <p>The name of the data quality monitoring job definition to describe.</p>
     */ JobDefinitionName : string | undefined ; } export namespace DescribeDataQualityJobDefinitionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeDataQualityJobDefinitionResponse { /**
     * <p>The Amazon Resource Name (ARN) of the data quality monitoring job definition.</p>
     */ JobDefinitionArn : string | undefined ; /**
     * <p>The name of the data quality monitoring job definition.</p>
     */ JobDefinitionName : string | undefined ; /**
     * <p>The time that the data quality monitoring job definition was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>The constraints and baselines for the data quality monitoring job definition.</p>
     */ DataQualityBaselineConfig ? : DataQualityBaselineConfig ; /**
     * <p>Information about the container that runs the data quality monitoring job.</p>
     */ DataQualityAppSpecification : DataQualityAppSpecification | undefined ; /**
     * <p>The list of inputs for the data quality monitoring job. Currently endpoints are
     *          supported.</p>
     */ DataQualityJobInput : DataQualityJobInput | undefined ; /**
     * <p>The output configuration for monitoring jobs.</p>
     */ DataQualityJobOutputConfig : MonitoringOutputConfig | undefined ; /**
     * <p>Identifies the resources to deploy for a monitoring job.</p>
     */ JobResources : MonitoringResources | undefined ; /**
     * <p>The networking configuration for the data quality monitoring job.</p>
     */ NetworkConfig ? : MonitoringNetworkConfig ; /**
     * <p>The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to
     *          perform tasks on your behalf.</p>
     */ RoleArn : string | undefined ; /**
     * <p>A time limit for how long the monitoring job is allowed to run before stopping.</p>
     */ StoppingCondition ? : MonitoringStoppingCondition ; } export namespace DescribeDataQualityJobDefinitionResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeDeviceRequest { /**
     * <p>Next token of device description.</p>
     */ NextToken ? : string ; /**
     * <p>The unique ID of the device.</p>
     */ DeviceName : string | undefined ; /**
     * <p>The name of the fleet the devices belong to.</p>
     */ DeviceFleetName : string | undefined ; } export namespace DescribeDeviceRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The model on the edge device.</p>
 */ export interface EdgeModel { /**
     * <p>The name of the model.</p>
     */ ModelName : string | undefined ; /**
     * <p>The model version.</p>
     */ ModelVersion : string | undefined ; /**
     * <p>The timestamp of the last data sample taken.</p>
     */ LatestSampleTime ? : Date ; /**
     * <p>The timestamp of the last inference that was made.</p>
     */ LatestInference ? : Date ; } export namespace EdgeModel { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeDeviceResponse { /**
     * <p>The Amazon Resource Name (ARN) of the device.</p>
     */ DeviceArn ? : string ; /**
     * <p>The unique identifier of the device.</p>
     */ DeviceName : string | undefined ; /**
     * <p>A description of the device.</p>
     */ Description ? : string ; /**
     * <p>The name of the fleet the device belongs to.</p>
     */ DeviceFleetName : string | undefined ; /**
     * <p>The Amazon Web Services Internet of Things (IoT) object thing name associated with the device.</p>
     */ IotThingName ? : string ; /**
     * <p>The timestamp of the last registration or de-reregistration.</p>
     */ RegistrationTime : Date | undefined ; /**
     * <p>The last heartbeat received from the device.</p>
     */ LatestHeartbeat ? : Date ; /**
     * <p>Models on the device.</p>
     */ Models ? : EdgeModel [ ] ; /**
     * <p>The maximum number of models.</p>
     */ MaxModels ? : number ; /**
     * <p>The response from the last list when returning a list large enough to need tokening.</p>
     */ NextToken ? : string ; /**
     * <p>Edge Manager agent version.</p>
     */ AgentVersion ? : string ; } export namespace DescribeDeviceResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeDeviceFleetRequest { /**
     * <p>The name of the fleet.</p>
     */ DeviceFleetName : string | undefined ; } export namespace DescribeDeviceFleetRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeDeviceFleetResponse { /**
     * <p>The name of the fleet.</p>
     */ DeviceFleetName : string | undefined ; /**
     * <p>The The Amazon Resource Name (ARN) of the fleet.</p>
     */ DeviceFleetArn : string | undefined ; /**
     * <p>The output configuration for storing sampled data.</p>
     */ OutputConfig : EdgeOutputConfig | undefined ; /**
     * <p>A description of the fleet.</p>
     */ Description ? : string ; /**
     * <p>Timestamp of when the device fleet was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>Timestamp of when the device fleet was last updated.</p>
     */ LastModifiedTime : Date | undefined ; /**
     * <p>The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).</p>
     */ RoleArn ? : string ; /**
     * <p>The Amazon Resource Name (ARN) alias created in Amazon Web Services Internet of Things (IoT).</p>
     */ IotRoleAlias ? : string ; } export namespace DescribeDeviceFleetResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeDomainRequest { /**
     * <p>The domain ID.</p>
     */ DomainId : string | undefined ; } export namespace DescribeDomainRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum DomainStatus { Delete_Failed = "Delete_Failed" , Deleting = "Deleting" , Failed = "Failed" , InService = "InService" , Pending = "Pending" , Update_Failed = "Update_Failed" , Updating = "Updating" } export interface DescribeDomainResponse { /**
     * <p>The domain's Amazon Resource Name (ARN).</p>
     */ DomainArn ? : string ; /**
     * <p>The domain ID.</p>
     */ DomainId ? : string ; /**
     * <p>The domain name.</p>
     */ DomainName ? : string ; /**
     * <p>The ID of the Amazon Elastic File System (EFS) managed by this Domain.</p>
     */ HomeEfsFileSystemId ? : string ; /**
     * <p>The SSO managed application instance ID.</p>
     */ SingleSignOnManagedApplicationInstanceId ? : string ; /**
     * <p>The status.</p>
     */ Status ? : DomainStatus | string ; /**
     * <p>The creation time.</p>
     */ CreationTime ? : Date ; /**
     * <p>The last modified time.</p>
     */ LastModifiedTime ? : Date ; /**
     * <p>The failure reason.</p>
     */ FailureReason ? : string ; /**
     * <p>The domain's authentication mode.</p>
     */ AuthMode ? : AuthMode | string ; /**
     * <p>Settings which are applied to UserProfiles in this domain if settings are not explicitly specified
     *            in a given UserProfile.
     *        </p>
     */ DefaultUserSettings ? : UserSettings ; /**
     * <p>Specifies the VPC used for non-EFS traffic. The default value is
     *         <code>PublicInternetOnly</code>.</p>
     *          <ul>
     *             <li>
     *                <p>
     *                   <code>PublicInternetOnly</code> - Non-EFS traffic is through a VPC managed by
     *             Amazon SageMaker, which allows direct internet access</p>
     *             </li>
     *             <li>
     *                <p>
     *                   <code>VpcOnly</code> - All Studio traffic is through the specified VPC and subnets</p>
     *             </li>
     *          </ul>
     */ AppNetworkAccessType ? : AppNetworkAccessType | string ; deprecated HomeEfsFileSystemKmsKeyId ? : string ; /**
     * <p>The VPC subnets that Studio uses for communication.</p>
     */ SubnetIds ? : string [ ] ; /**
     * <p>The domain's URL.</p>
     */ Url ? : string ; /**
     * <p>The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.</p>
     */ VpcId ? : string ; /**
     * <p>The Amazon Web Services KMS customer managed key used to encrypt
     *          the EFS volume attached to the domain.</p>
     */ KmsKeyId ? : string ; /**
     * <p>A collection of <code>Domain</code> settings.</p>
     */ DomainSettings ? : DomainSettings ; /**
     * <p>The entity that creates and manages the required security groups for inter-app communication in <code>VPCOnly</code> mode.
     *             Required when <code>CreateDomain.AppNetworkAccessType</code> is <code>VPCOnly</code> and <code>DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn</code> is provided.</p>
     */ AppSecurityGroupManagement ? : AppSecurityGroupManagement | string ; /**
     * <p>The ID of the security group that authorizes traffic between the <code>RSessionGateway</code> apps and the <code>RStudioServerPro</code> app.</p>
     */ SecurityGroupIdForDomainBoundary ? : string ; } export namespace DescribeDomainResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeEdgePackagingJobRequest { /**
     * <p>The name of the edge packaging job.</p>
     */ EdgePackagingJobName : string | undefined ; } export namespace DescribeEdgePackagingJobRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum EdgePackagingJobStatus { Completed = "COMPLETED" , Failed = "FAILED" , InProgress = "INPROGRESS" , Starting = "STARTING" , Stopped = "STOPPED" , Stopping = "STOPPING" } export enum EdgePresetDeploymentStatus { Completed = "COMPLETED" , Failed = "FAILED" } /**
 * <p>The output of a SageMaker Edge Manager deployable resource.</p>
 */ export interface EdgePresetDeploymentOutput { /**
     * <p>The deployment type created by SageMaker Edge Manager. Currently only
     *      supports Amazon Web Services IoT Greengrass Version 2 components.</p>
     */ Type : EdgePresetDeploymentType | string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the generated deployable resource.</p>
     */ Artifact ? : string ; /**
     * <p>The status of the deployable resource.</p>
     */ Status ? : EdgePresetDeploymentStatus | string ; /**
     * <p>Returns a message describing the status of the deployed resource.</p>
     */ StatusMessage ? : string ; } export namespace EdgePresetDeploymentOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeEdgePackagingJobResponse { /**
     * <p>The Amazon Resource Name (ARN) of the edge packaging job.</p>
     */ EdgePackagingJobArn : string | undefined ; /**
     * <p>The name of the edge packaging job.</p>
     */ EdgePackagingJobName : string | undefined ; /**
     * <p>The name of the SageMaker Neo compilation job that is used to locate model artifacts that are being packaged.</p>
     */ CompilationJobName ? : string ; /**
     * <p>The name of the model.</p>
     */ ModelName ? : string ; /**
     * <p>The version of the model.</p>
     */ ModelVersion ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to  download and upload the model, and to contact Neo.</p>
     */ RoleArn ? : string ; /**
     * <p>The output configuration for the edge packaging job.</p>
     */ OutputConfig ? : EdgeOutputConfig ; /**
     * <p>The Amazon Web Services KMS key to use when encrypting the EBS volume the job run on.</p>
     */ ResourceKey ? : string ; /**
     * <p>The current status of the packaging job.</p>
     */ EdgePackagingJobStatus : EdgePackagingJobStatus | string | undefined ; /**
     * <p>Returns a message describing the job status and error messages.</p>
     */ EdgePackagingJobStatusMessage ? : string ; /**
     * <p>The timestamp of when the packaging job was created.</p>
     */ CreationTime ? : Date ; /**
     * <p>The timestamp of when the job was last updated.</p>
     */ LastModifiedTime ? : Date ; /**
     * <p>The Amazon Simple Storage (S3) URI where model artifacts ares stored.</p>
     */ ModelArtifact ? : string ; /**
     * <p>The signature document of files in the model artifact.</p>
     */ ModelSignature ? : string ; /**
     * <p>The output of a SageMaker Edge Manager deployable resource.</p>
     */ PresetDeploymentOutput ? : EdgePresetDeploymentOutput ; } export namespace DescribeEdgePackagingJobResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeEndpointInput { /**
     * <p>The name of the endpoint.</p>
     */ EndpointName : string | undefined ; } export namespace DescribeEndpointInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum EndpointStatus { CREATING = "Creating" , DELETING = "Deleting" , FAILED = "Failed" , IN_SERVICE = "InService" , OUT_OF_SERVICE = "OutOfService" , ROLLING_BACK = "RollingBack" , SYSTEM_UPDATING = "SystemUpdating" , UPDATING = "Updating" } export enum VariantStatus { ACTIVATING_TRAFFIC = "ActivatingTraffic" , BAKING = "Baking" , CREATING = "Creating" , DELETING = "Deleting" , UPDATING = "Updating" } /**
 * <p>Describes the status of the production variant.</p>
 */ export interface ProductionVariantStatus { /**
     * <p>The endpoint variant status which describes the current deployment stage status or operational status.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>Creating</code>: Creating inference resources for the production variant.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>Deleting</code>: Terminating inference resources for the production variant.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>Updating</code>: Updating capacity for the production variant.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>ActivatingTraffic</code>: Turning on traffic for the production variant.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>Baking</code>: Waiting period to monitor the CloudWatch alarms in the
     *                 automatic rollback configuration.</p>
     *             </li>
     *          </ul>
     */ Status : VariantStatus | string | undefined ; /**
     * <p>A message that describes the status of the production variant.</p>
     */ StatusMessage ? : string ; /**
     * <p>The start time of the current status change.</p>
     */ StartTime ? : Date ; } export namespace ProductionVariantStatus { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The production variant summary for a deployment when an endpoint is
 *             creating or updating with the <code>
 *                <a>CreateEndpoint</a>
 *             </code>
 *             or <code>
 *                <a>UpdateEndpoint</a>
 *             </code> operations.
 *             Describes the <code>VariantStatus </code>, weight and capacity for a production
 *             variant associated with an endpoint.
 *            </p>
 */ export interface PendingProductionVariantSummary { /**
     * <p>The name of the variant.</p>
     */ VariantName : string | undefined ; /**
     * <p>An array of <code>DeployedImage</code> objects that specify the Amazon EC2
     *             Container Registry paths of the inference images deployed on instances of this
     *             <code>ProductionVariant</code>.</p>
     */ DeployedImages ? : DeployedImage [ ] ; /**
     * <p>The weight associated with the variant.</p>
     */ CurrentWeight ? : number ; /**
     * <p>The requested weight for the variant in this deployment, as specified in the endpoint configuration
     *             for the endpoint. The value is taken from the request to the <code>
     *                <a>CreateEndpointConfig</a>
     *             </code> operation.</p>
     */ DesiredWeight ? : number ; /**
     * <p>The number of instances associated with the variant.</p>
     */ CurrentInstanceCount ? : number ; /**
     * <p>The number of instances requested in this deployment, as specified in the endpoint configuration
     *             for the endpoint. The value is taken from the request to the <code>
     *                <a>CreateEndpointConfig</a>
     *             </code> operation.</p>
     */ DesiredInstanceCount ? : number ; /**
     * <p>The type of instances associated with the variant.</p>
     */ InstanceType ? : ProductionVariantInstanceType | string ; /**
     * <p>The size of the Elastic Inference (EI) instance to use for the production variant. EI
     *             instances provide on-demand GPU computing for inference. For more information, see
     *             <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html">Using Elastic
     *                 Inference in Amazon SageMaker</a>.</p>
     */ AcceleratorType ? : ProductionVariantAcceleratorType | string ; /**
     * <p>The endpoint variant status which describes the current deployment stage status or operational status.</p>
     */ VariantStatus ? : ProductionVariantStatus [ ] ; /**
     * <p>The serverless configuration for the endpoint.</p>
     *          <note>
     *             <p>Serverless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.</p>
     *          </note>
     */ CurrentServerlessConfig ? : ProductionVariantServerlessConfig ; /**
     * <p>The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.</p>
     *          <note>
     *             <p>Serverless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.</p>
     *          </note>
     */ DesiredServerlessConfig ? : ProductionVariantServerlessConfig ; } export namespace PendingProductionVariantSummary { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The summary of an in-progress deployment when an endpoint is creating or
 *             updating with a new endpoint configuration.</p>
 */ export interface PendingDeploymentSummary { /**
     * <p>The name of the endpoint configuration used in the deployment. </p>
     */ EndpointConfigName : string | undefined ; /**
     * <p>List of <code>PendingProductionVariantSummary</code> objects.</p>
     */ ProductionVariants ? : PendingProductionVariantSummary [ ] ; /**
     * <p>The start time of the deployment.</p>
     */ StartTime ? : Date ; } export namespace PendingDeploymentSummary { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Describes weight and capacities for a production variant associated with an
 *             endpoint. If you sent a request to the <code>UpdateEndpointWeightsAndCapacities</code>
 *             API and the endpoint status is <code>Updating</code>, you get different desired and
 *             current values. </p>
 */ export interface ProductionVariantSummary { /**
     * <p>The name of the variant.</p>
     */ VariantName : string | undefined ; /**
     * <p>An array of <code>DeployedImage</code> objects that specify the Amazon EC2 Container Registry paths of the
     *             inference images deployed on instances of this <code>ProductionVariant</code>.</p>
     */ DeployedImages ? : DeployedImage [ ] ; /**
     * <p>The weight associated with the variant.</p>
     */ CurrentWeight ? : number ; /**
     * <p>The requested weight, as specified in the
     *                 <code>UpdateEndpointWeightsAndCapacities</code> request. </p>
     */ DesiredWeight ? : number ; /**
     * <p>The number of instances associated with the variant.</p>
     */ CurrentInstanceCount ? : number ; /**
     * <p>The number of instances requested in the
     *                 <code>UpdateEndpointWeightsAndCapacities</code> request. </p>
     */ DesiredInstanceCount ? : number ; /**
     * <p>The endpoint variant status which describes the current deployment stage status or operational status.</p>
     */ VariantStatus ? : ProductionVariantStatus [ ] ; /**
     * <p>The serverless configuration for the endpoint.</p>
     *          <note>
     *             <p>Serverless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.</p>
     *          </note>
     */ CurrentServerlessConfig ? : ProductionVariantServerlessConfig ; /**
     * <p>The serverless configuration requested for the endpoint update.</p>
     *          <note>
     *             <p>Serverless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.</p>
     *          </note>
     */ DesiredServerlessConfig ? : ProductionVariantServerlessConfig ; } export namespace ProductionVariantSummary { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeEndpointOutput { /**
     * <p>Name of the endpoint.</p>
     */ EndpointName : string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the endpoint.</p>
     */ EndpointArn : string | undefined ; /**
     * <p>The name of the endpoint configuration associated with this endpoint.</p>
     */ EndpointConfigName : string | undefined ; /**
     * <p> An array of <a>ProductionVariantSummary</a> objects, one for each model
     *             hosted behind this endpoint. </p>
     */ ProductionVariants ? : ProductionVariantSummary [ ] ; /**
     * <p></p>
     */ DataCaptureConfig ? : DataCaptureConfigSummary ; /**
     * <p>The status of the endpoint.</p>
     *         <ul>
     *             <li>
     *                 <p>
     *                   <code>OutOfService</code>: Endpoint is not available to take incoming
     *                     requests.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>Creating</code>: <a>CreateEndpoint</a> is executing.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>Updating</code>: <a>UpdateEndpoint</a> or <a>UpdateEndpointWeightsAndCapacities</a> is executing.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>SystemUpdating</code>: Endpoint is undergoing maintenance and cannot be
     *                     updated or deleted or re-scaled until it has completed. This maintenance
     *                     operation does not change any customer-specified values such as VPC config, KMS
     *                     encryption, model, instance type, or instance count.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>RollingBack</code>: Endpoint fails to scale up or down or change its
     *                     variant weight and is in the process of rolling back to its previous
     *                     configuration. Once the rollback completes, endpoint returns to an
     *                         <code>InService</code> status. This transitional status only applies to an
     *                     endpoint that has autoscaling enabled and is undergoing variant weight or
     *                     capacity changes as part of an <a>UpdateEndpointWeightsAndCapacities</a> call or when the <a>UpdateEndpointWeightsAndCapacities</a> operation is called
     *                     explicitly.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>InService</code>: Endpoint is available to process incoming
     *                     requests.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>Deleting</code>: <a>DeleteEndpoint</a> is executing.</p>
     *             </li>
     *             <li>
     *                 <p>
     *                   <code>Failed</code>: Endpoint could not be created, updated, or re-scaled. Use
     *                         <a>DescribeEndpointOutput$FailureReason</a> for information about
     *                     the failure. <a>DeleteEndpoint</a> is the only operation that can be
     *                     performed on a failed endpoint.</p>
     *             </li>
     *          </ul>
     */ EndpointStatus : EndpointStatus | string | undefined ; /**
     * <p>If the status of the endpoint is <code>Failed</code>, the reason why it failed.
     *         </p>
     */ FailureReason ? : string ; /**
     * <p>A timestamp that shows when the endpoint was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>A timestamp that shows when the endpoint was last modified.</p>
     */ LastModifiedTime : Date | undefined ; /**
     * <p>The most recent deployment configuration for the endpoint.</p>
     */ LastDeploymentConfig ? : DeploymentConfig ; /**
     * <p>Returns the description of an endpoint configuration created
     *             using the <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html">
     *                <code>CreateEndpointConfig</code>
     *             </a> API.</p>
     */ AsyncInferenceConfig ? : AsyncInferenceConfig ; /**
     * <p>Returns the summary of an in-progress deployment. This field is only returned when the
     *             endpoint is creating or updating with a new endpoint configuration.</p>
     */ PendingDeploymentSummary ? : PendingDeploymentSummary ; } export namespace DescribeEndpointOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeEndpointConfigInput { /**
     * <p>The name of the endpoint configuration.</p>
     */ EndpointConfigName : string | undefined ; } export namespace DescribeEndpointConfigInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeEndpointConfigOutput { /**
     * <p>Name of the Amazon SageMaker endpoint configuration.</p>
     */ EndpointConfigName : string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the endpoint configuration.</p>
     */ EndpointConfigArn : string | undefined ; /**
     * <p>An array of <code>ProductionVariant</code> objects, one for each model that you
     *             want to host at this endpoint.</p>
     */ ProductionVariants : ProductionVariant [ ] | undefined ; /**
     * <p></p>
     */ DataCaptureConfig ? : DataCaptureConfig ; /**
     * <p>Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage
     *             volume attached to the instance.</p>
     */ KmsKeyId ? : string ; /**
     * <p>A timestamp that shows when the endpoint configuration was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>Returns the description of an endpoint configuration created using the
     *             <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html">
     *                <code>CreateEndpointConfig</code>
     *             </a> API.</p>
     */ AsyncInferenceConfig ? : AsyncInferenceConfig ; } export namespace DescribeEndpointConfigOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeExperimentRequest { /**
     * <p>The name of the experiment to describe.</p>
     */ ExperimentName : string | undefined ; } export namespace DescribeExperimentRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The source of the experiment.</p>
 */ export interface ExperimentSource { /**
     * <p>The Amazon Resource Name (ARN) of the source.</p>
     */ SourceArn : string | undefined ; /**
     * <p>The source type.</p>
     */ SourceType ? : string ; } export namespace ExperimentSource { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeExperimentResponse { /**
     * <p>The name of the experiment.</p>
     */ ExperimentName ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of the experiment.</p>
     */ ExperimentArn ? : string ; /**
     * <p>The name of the experiment as displayed. If <code>DisplayName</code> isn't specified,
     *         <code>ExperimentName</code> is displayed.</p>
     */ DisplayName ? : string ; /**
     * <p>The ARN of the source and, optionally, the type.</p>
     */ Source ? : ExperimentSource ; /**
     * <p>The description of the experiment.</p>
     */ Description ? : string ; /**
     * <p>When the experiment was created.</p>
     */ CreationTime ? : Date ; /**
     * <p>Who created the experiment.</p>
     */ CreatedBy ? : UserContext ; /**
     * <p>When the experiment was last modified.</p>
     */ LastModifiedTime ? : Date ; /**
     * <p>Who last modified the experiment.</p>
     */ LastModifiedBy ? : UserContext ; } export namespace DescribeExperimentResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeFeatureGroupRequest { /**
     * <p>The name of the <code>FeatureGroup</code> you want described. </p>
     */ FeatureGroupName : string | undefined ; /**
     * <p>A token to resume pagination of the list of <code>Features</code>
     *             (<code>FeatureDefinitions</code>). 2,500 <code>Features</code> are returned by
     *          default.</p>
     */ NextToken ? : string ; } export namespace DescribeFeatureGroupRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum FeatureGroupStatus { CREATED = "Created" , CREATE_FAILED = "CreateFailed" , CREATING = "Creating" , DELETE_FAILED = "DeleteFailed" , DELETING = "Deleting" } export enum OfflineStoreStatusValue { ACTIVE = "Active" , BLOCKED = "Blocked" , DISABLED = "Disabled" } /**
 * <p>The status of <code>OfflineStore</code>.</p>
 */ export interface OfflineStoreStatus { /**
     * <p>An <code>OfflineStore</code> status.</p>
     */ Status : OfflineStoreStatusValue | string | undefined ; /**
     * <p>The justification for why the OfflineStoreStatus is Blocked (if applicable).</p>
     */ BlockedReason ? : string ; } export namespace OfflineStoreStatus { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeFeatureGroupResponse { /**
     * <p>The Amazon Resource Name (ARN) of the <code>FeatureGroup</code>.  </p>
     */ FeatureGroupArn : string | undefined ; /**
     * <p>he name of the <code>FeatureGroup</code>.</p>
     */ FeatureGroupName : string | undefined ; /**
     * <p>The name of the <code>Feature</code> used for <code>RecordIdentifier</code>, whose value
     *          uniquely identifies a record stored in the feature store.</p>
     */ RecordIdentifierFeatureName : string | undefined ; /**
     * <p>The name of the feature that stores the <code>EventTime</code> of a Record in a
     *          <code>FeatureGroup</code>.</p>
     *          <p> An <code>EventTime</code> is a point in time when a new event occurs that
     *          corresponds to the creation or update of a <code>Record</code> in a
     *             <code>FeatureGroup</code>. All <code>Records</code> in the <code>FeatureGroup</code>
     *          have a corresponding <code>EventTime</code>.</p>
     */ EventTimeFeatureName : string | undefined ; /**
     * <p>A list of the <code>Features</code> in the <code>FeatureGroup</code>.
     *          Each feature is defined by a <code>FeatureName</code> and <code>FeatureType</code>.</p>
     */ FeatureDefinitions : FeatureDefinition [ ] | undefined ; /**
     * <p>A timestamp indicating when SageMaker created the <code>FeatureGroup</code>.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>The configuration for the <code>OnlineStore</code>.</p>
     */ OnlineStoreConfig ? : OnlineStoreConfig ; /**
     * <p>The configuration of the <code>OfflineStore</code>, inducing the S3 location of the
     *          <code>OfflineStore</code>, Amazon Web Services Glue or Amazon Web Services Hive data catalogue configurations, and the
     *          security configuration.</p>
     */ OfflineStoreConfig ? : OfflineStoreConfig ; /**
     * <p>The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the
     *          <code>OfflineStore</code> if an <code>OfflineStoreConfig</code> is provided.</p>
     */ RoleArn ? : string ; /**
     * <p>The status of the feature group.</p>
     */ FeatureGroupStatus ? : FeatureGroupStatus | string ; /**
     * <p>The status of the <code>OfflineStore</code>. Notifies you if replicating data into the
     *             <code>OfflineStore</code> has failed. Returns either: <code>Active</code> or
     *             <code>Blocked</code>
     *          </p>
     */ OfflineStoreStatus ? : OfflineStoreStatus ; /**
     * <p>The reason that the <code>FeatureGroup</code> failed to be replicated in the
     *             <code>OfflineStore</code>. This is failure can occur because:</p>
     *          <ul>
     *             <li>
     *                <p>The <code>FeatureGroup</code> could not be created in the
     *                   <code>OfflineStore</code>.</p>
     *             </li>
     *             <li>
     *                <p>The <code>FeatureGroup</code> could not be deleted from the
     *                   <code>OfflineStore</code>.</p>
     *             </li>
     *          </ul>
     */ FailureReason ? : string ; /**
     * <p>A free form description of the feature group.</p>
     */ Description ? : string ; /**
     * <p>A token to resume pagination of the list of <code>Features</code>
     *             (<code>FeatureDefinitions</code>).</p>
     */ NextToken : string | undefined ; } export namespace DescribeFeatureGroupResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeFlowDefinitionRequest { /**
     * <p>The name of the flow definition.</p>
     */ FlowDefinitionName : string | undefined ; } export namespace DescribeFlowDefinitionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum FlowDefinitionStatus { ACTIVE = "Active" , DELETING = "Deleting" , FAILED = "Failed" , INITIALIZING = "Initializing" } export interface DescribeFlowDefinitionResponse { /**
     * <p>The Amazon Resource Name (ARN) of the flow defintion.</p>
     */ FlowDefinitionArn : string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the flow definition.</p>
     */ FlowDefinitionName : string | undefined ; /**
     * <p>The status of the flow definition. Valid values are listed below.</p>
     */ FlowDefinitionStatus : FlowDefinitionStatus | string | undefined ; /**
     * <p>The timestamp when the flow definition was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>Container for configuring the source of human task requests. Used to specify if
     *       Amazon Rekognition or Amazon Textract is used as an integration source.</p>
     */ HumanLoopRequestSource ? : HumanLoopRequestSource ; /**
     * <p>An object containing information about what triggers a human review workflow.</p>
     */ HumanLoopActivationConfig ? : HumanLoopActivationConfig ; /**
     * <p>An object containing information about who works on the task, the workforce task price, and other task details.</p>
     */ HumanLoopConfig : HumanLoopConfig | undefined ; /**
     * <p>An object containing information about the output file.</p>
     */ OutputConfig : FlowDefinitionOutputConfig | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) execution role for the flow definition.</p>
     */ RoleArn : string | undefined ; /**
     * <p>The reason your flow definition failed.</p>
     */ FailureReason ? : string ; } export namespace DescribeFlowDefinitionResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeHumanTaskUiRequest { /**
     * <p>The name of the human task user interface
     *       (worker task template) you want information about.</p>
     */ HumanTaskUiName : string | undefined ; } export namespace DescribeHumanTaskUiRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum HumanTaskUiStatus { ACTIVE = "Active" , DELETING = "Deleting" } /**
 * <p>Container for user interface template information.</p>
 */ export interface UiTemplateInfo { /**
     * <p>The URL for the user interface template.</p>
     */ Url ? : string ; /**
     * <p>The SHA-256 digest of the contents of the template.</p>
     */ ContentSha256 ? : string ; } export namespace UiTemplateInfo { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeHumanTaskUiResponse { /**
     * <p>The Amazon Resource Name (ARN) of the human task user interface (worker task template).</p>
     */ HumanTaskUiArn : string | undefined ; /**
     * <p>The name of the human task user interface (worker task template).</p>
     */ HumanTaskUiName : string | undefined ; /**
     * <p>The status of the human task user interface (worker task template). Valid values are listed below.</p>
     */ HumanTaskUiStatus ? : HumanTaskUiStatus | string ; /**
     * <p>The timestamp when the human task user interface was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>Container for user interface template information.</p>
     */ UiTemplate : UiTemplateInfo | undefined ; } export namespace DescribeHumanTaskUiResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeHyperParameterTuningJobRequest { /**
     * <p>The name of the tuning job.</p>
     */ HyperParameterTuningJobName : string | undefined ; } export namespace DescribeHyperParameterTuningJobRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Shows the final value for the
 *             objective
 *             metric for a training job that was launched by a hyperparameter
 *             tuning job. You define the objective metric in the
 *                 <code>HyperParameterTuningJobObjective</code> parameter of <a>HyperParameterTuningJobConfig</a>.</p>
 */ export interface FinalHyperParameterTuningJobObjectiveMetric { /**
     * <p>Whether to
     *             minimize
     *             or maximize the objective metric. Valid values are Minimize and
     *             Maximize.</p>
     */ Type ? : HyperParameterTuningJobObjectiveType | string ; /**
     * <p>The name of the
     *             objective
     *             metric.</p>
     */ MetricName : string | undefined ; /**
     * <p>The value of the objective metric.</p>
     */ Value : number | undefined ; } export namespace FinalHyperParameterTuningJobObjectiveMetric { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum TrainingJobStatus { COMPLETED = "Completed" , FAILED = "Failed" , IN_PROGRESS = "InProgress" , STOPPED = "Stopped" , STOPPING = "Stopping" } /**
 * <p>Specifies
 *             summary information about a training job.</p>
 */ export interface HyperParameterTrainingJobSummary { /**
     * <p>The training job definition name.</p>
     */ TrainingJobDefinitionName ? : string ; /**
     * <p>The name of the training job.</p>
     */ TrainingJobName : string | undefined ; /**
     * <p>The
     *             Amazon
     *             Resource Name (ARN) of the training job.</p>
     */ TrainingJobArn : string | undefined ; /**
     * <p>The HyperParameter tuning job that launched the training job.</p>
     */ TuningJobName ? : string ; /**
     * <p>The date and time that the training job was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>The date and time that the training job started.</p>
     */ TrainingStartTime ? : Date ; /**
     * <p>Specifies the time when the training job ends on training instances. You are billed
     *             for the time interval between the value of <code>TrainingStartTime</code> and this time.
     *             For successful jobs and stopped jobs, this is the time after model artifacts are
     *             uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.</p>
     */ TrainingEndTime ? : Date ; /**
     * <p>The
     *             status
     *             of the training job.</p>
     */ TrainingJobStatus : TrainingJobStatus | string | undefined ; /**
     * <p>A
     *             list of the hyperparameters for which you specified ranges to
     *             search.</p>
     */ TunedHyperParameters : { [ key ] : string ; } | undefined ; /**
     * <p>The
     *             reason that the training job failed.
     *         </p>
     */ FailureReason ? : string ; /**
     * <p>The <a>FinalHyperParameterTuningJobObjectiveMetric</a> object that
     *             specifies the
     *             value
     *             of the
     *             objective
     *             metric of the tuning job that launched this training job.</p>
     */ FinalHyperParameterTuningJobObjectiveMetric ? : FinalHyperParameterTuningJobObjectiveMetric ; /**
     * <p>The status of the objective metric for the training job:</p>
     *         <ul>
     *             <li>
     *                 <p>Succeeded: The
     *                     final
     *                     objective metric for the training job was evaluated by the
     *                     hyperparameter tuning job and
     *                     used
     *                     in the hyperparameter tuning process.</p>
     *             </li>
     *          </ul>
     *         <ul>
     *             <li>
     *                 <p>Pending: The training job is in progress and evaluation of its final objective
     *                     metric is pending.</p>
     *             </li>
     *          </ul>
     *         <ul>
     *             <li>
     *                 <p>Failed:
     *                     The final objective metric for the training job was not evaluated, and was not
     *                     used in the hyperparameter tuning process. This typically occurs when the
     *                     training job failed or did not emit an objective
     *                     metric.</p>
     *             </li>
     *          </ul>
     */ ObjectiveStatus ? : ObjectiveStatus | string ; } export namespace HyperParameterTrainingJobSummary { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum HyperParameterTuningJobStatus { COMPLETED = "Completed" , FAILED = "Failed" , IN_PROGRESS = "InProgress" , STOPPED = "Stopped" , STOPPING = "Stopping" } /**
 * <p>Specifies the number of training jobs that this hyperparameter tuning job launched,
 *             categorized by the status of their objective metric. The objective metric status shows
 *             whether the
 *             final
 *             objective metric for the training job has been evaluated by the
 *             tuning job and used in the hyperparameter tuning process.</p>
 */ export interface ObjectiveStatusCounters { /**
     * <p>The number of training jobs whose final objective metric was evaluated by the
     *             hyperparameter tuning job and used in the hyperparameter tuning process.</p>
     */ Succeeded ? : number ; /**
     * <p>The number of training jobs that are in progress and pending evaluation of their final
     *             objective metric.</p>
     */ Pending ? : number ; /**
     * <p>The number of training jobs whose final objective metric was not evaluated and used in
     *             the hyperparameter tuning process. This typically occurs when the training job failed or
     *             did not emit an objective metric.</p>
     */ Failed ? : number ; } export namespace ObjectiveStatusCounters { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The numbers of training jobs launched by a hyperparameter tuning job, categorized by
 *             status.</p>
 */ export interface TrainingJobStatusCounters { /**
     * <p>The number of completed training jobs launched by the hyperparameter tuning
     *             job.</p>
     */ Completed ? : number ; /**
     * <p>The number of in-progress training jobs launched by a hyperparameter tuning
     *             job.</p>
     */ InProgress ? : number ; /**
     * <p>The number of training jobs that failed, but can be retried. A failed training job can
     *             be retried only if it failed because an internal service error occurred.</p>
     */ RetryableError ? : number ; /**
     * <p>The number of training jobs that failed and can't be retried. A failed training job
     *             can't be retried if it failed because a client error occurred.</p>
     */ NonRetryableError ? : number ; /**
     * <p>The number of training jobs launched by a hyperparameter tuning job that were
     *             manually
     *             stopped.</p>
     */ Stopped ? : number ; } export namespace TrainingJobStatusCounters { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeHyperParameterTuningJobResponse { /**
     * <p>The name of the tuning job.</p>
     */ HyperParameterTuningJobName : string | undefined ; /**
     * <p>The
     *             Amazon Resource Name (ARN) of the tuning job.</p>
     */ HyperParameterTuningJobArn : string | undefined ; /**
     * <p>The <a>HyperParameterTuningJobConfig</a> object that specifies the
     *             configuration of the tuning job.</p>
     */ HyperParameterTuningJobConfig : HyperParameterTuningJobConfig | undefined ; /**
     * <p>The <a>HyperParameterTrainingJobDefinition</a> object that specifies the
     *             definition of the training jobs that this tuning job launches.</p>
     */ TrainingJobDefinition ? : HyperParameterTrainingJobDefinition ; /**
     * <p>A list of the <a>HyperParameterTrainingJobDefinition</a> objects launched
     *             for this tuning job.</p>
     */ TrainingJobDefinitions ? : HyperParameterTrainingJobDefinition [ ] ; /**
     * <p>The status of the tuning job: InProgress, Completed, Failed, Stopping, or
     *             Stopped.</p>
     */ HyperParameterTuningJobStatus : HyperParameterTuningJobStatus | string | undefined ; /**
     * <p>The date and time that the tuning job started.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>The date and time that the tuning job ended.</p>
     */ HyperParameterTuningEndTime ? : Date ; /**
     * <p>The date and time that the status of the tuning job was modified. </p>
     */ LastModifiedTime ? : Date ; /**
     * <p>The <a>TrainingJobStatusCounters</a> object that specifies the number of
     *             training jobs, categorized by status, that this tuning job launched.</p>
     */ TrainingJobStatusCounters : TrainingJobStatusCounters | undefined ; /**
     * <p>The <a>ObjectiveStatusCounters</a> object that specifies the number of
     *             training jobs, categorized by the status of their final objective metric, that this
     *             tuning job launched.</p>
     */ ObjectiveStatusCounters : ObjectiveStatusCounters | undefined ; /**
     * <p>A <a>TrainingJobSummary</a> object that describes the training job that
     *             completed with the best current <a>HyperParameterTuningJobObjective</a>.</p>
     */ BestTrainingJob ? : HyperParameterTrainingJobSummary ; /**
     * <p>If the hyperparameter tuning job is an warm start tuning job with a
     *                 <code>WarmStartType</code> of <code>IDENTICAL_DATA_AND_ALGORITHM</code>, this is the
     *                 <a>TrainingJobSummary</a> for the training job with the best objective
     *             metric value of all training jobs launched by this tuning job and all parent jobs
     *             specified for the warm start tuning job.</p>
     */ OverallBestTrainingJob ? : HyperParameterTrainingJobSummary ; /**
     * <p>The configuration for starting the hyperparameter parameter tuning job using one or
     *             more previous tuning jobs as a starting point. The results of previous tuning jobs are
     *             used to inform which combinations of hyperparameters to search over in the new tuning
     *             job.</p>
     */ WarmStartConfig ? : HyperParameterTuningJobWarmStartConfig ; /**
     * <p>If the tuning job failed, the reason it failed.</p>
     */ FailureReason ? : string ; } export namespace DescribeHyperParameterTuningJobResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeImageRequest { /**
     * <p>The name of the image to describe.</p>
     */ ImageName : string | undefined ; } export namespace DescribeImageRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum ImageStatus { CREATED = "CREATED" , CREATE_FAILED = "CREATE_FAILED" , CREATING = "CREATING" , DELETE_FAILED = "DELETE_FAILED" , DELETING = "DELETING" , UPDATE_FAILED = "UPDATE_FAILED" , UPDATING = "UPDATING" } export interface DescribeImageResponse { /**
     * <p>When the image was created.</p>
     */ CreationTime ? : Date ; /**
     * <p>The description of the image.</p>
     */ Description ? : string ; /**
     * <p>The name of the image as displayed.</p>
     */ DisplayName ? : string ; /**
     * <p>When a create, update, or delete operation fails, the reason for the failure.</p>
     */ FailureReason ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of the image.</p>
     */ ImageArn ? : string ; /**
     * <p>The name of the image.</p>
     */ ImageName ? : string ; /**
     * <p>The status of the image.</p>
     */ ImageStatus ? : ImageStatus | string ; /**
     * <p>When the image was last modified.</p>
     */ LastModifiedTime ? : Date ; /**
     * <p>The Amazon Resource Name (ARN) of the IAM role that enables Amazon SageMaker to perform tasks on your behalf.</p>
     */ RoleArn ? : string ; } export namespace DescribeImageResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeImageVersionRequest { /**
     * <p>The name of the image.</p>
     */ ImageName : string | undefined ; /**
     * <p>The version of the image. If not specified, the latest version is described.</p>
     */ Version ? : number ; } export namespace DescribeImageVersionRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum ImageVersionStatus { CREATED = "CREATED" , CREATE_FAILED = "CREATE_FAILED" , CREATING = "CREATING" , DELETE_FAILED = "DELETE_FAILED" , DELETING = "DELETING" } export interface DescribeImageVersionResponse { /**
     * <p>The registry path of the container image on which this image version is based.</p>
     */ BaseImage ? : string ; /**
     * <p>The registry path of the container image that contains this image version.</p>
     */ ContainerImage ? : string ; /**
     * <p>When the version was created.</p>
     */ CreationTime ? : Date ; /**
     * <p>When a create or delete operation fails, the reason for the failure.</p>
     */ FailureReason ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of the image the version is based on.</p>
     */ ImageArn ? : string ; /**
     * <p>The ARN of the version.</p>
     */ ImageVersionArn ? : string ; /**
     * <p>The status of the version.</p>
     */ ImageVersionStatus ? : ImageVersionStatus | string ; /**
     * <p>When the version was last modified.</p>
     */ LastModifiedTime ? : Date ; /**
     * <p>The version number.</p>
     */ Version ? : number ; } export namespace DescribeImageVersionResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeInferenceRecommendationsJobRequest { /**
     * <p>The name of the job. The name must be unique within an
     *            Amazon Web Services Region in the Amazon Web Services account.</p>
     */ JobName : string | undefined ; } export namespace DescribeInferenceRecommendationsJobRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The endpoint configuration made by Inference Recommender during a recommendation job.</p>
 */ export interface EndpointOutputConfiguration { /**
     * <p>The name of the endpoint made during a recommendation job.</p>
     */ EndpointName : string | undefined ; /**
     * <p>The name of the production variant (deployed model) made during a recommendation job.</p>
     */ VariantName : string | undefined ; /**
     * <p>The instance type recommended by Amazon SageMaker Inference Recommender.</p>
     */ InstanceType : ProductionVariantInstanceType | string | undefined ; /**
     * <p>The number of instances recommended to launch initially.</p>
     */ InitialInstanceCount : number | undefined ; } export namespace EndpointOutputConfiguration { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>The metrics of recommendations.</p>
 */ export interface RecommendationMetrics { /**
     * <p>Defines the cost per hour for the instance. </p>
     */ CostPerHour : number | undefined ; /**
     * <p>Defines the cost per inference for the instance .</p>
     */ CostPerInference : number | undefined ; /**
     * <p>The expected maximum number of requests per minute for the instance.</p>
     */ MaxInvocations : number | undefined ; /**
     * <p>The expected model latency at maximum invocation per minute for the instance.</p>
     */ ModelLatency : number | undefined ; } export namespace RecommendationMetrics { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.</p>
 */ export interface EnvironmentParameter { /**
     * <p>The environment key suggested by the Amazon SageMaker Inference Recommender.</p>
     */ Key : string | undefined ; /**
     * <p>The value type suggested by the Amazon SageMaker Inference Recommender.</p>
     */ ValueType : string | undefined ; /**
     * <p>The value suggested by the Amazon SageMaker Inference Recommender.</p>
     */ Value : string | undefined ; } export namespace EnvironmentParameter { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Defines the model configuration. Includes the specification name and environment parameters.</p>
 */ export interface ModelConfiguration { /**
     * <p>The inference specification name in the model package version.</p>
     */ InferenceSpecificationName ? : string ; /**
     * <p>Defines the environment parameters that includes key, value types, and values.</p>
     */ EnvironmentParameters ? : EnvironmentParameter [ ] ; } export namespace ModelConfiguration { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>A list of recommendations made by Amazon SageMaker Inference Recommender.</p>
 */ export interface InferenceRecommendation { /**
     * <p>The metrics used to decide what recommendation to make.</p>
     */ Metrics : RecommendationMetrics | undefined ; /**
     * <p>Defines the endpoint configuration parameters.</p>
     */ EndpointConfiguration : EndpointOutputConfiguration | undefined ; /**
     * <p>Defines the model configuration.</p>
     */ ModelConfiguration : ModelConfiguration | undefined ; } export namespace InferenceRecommendation { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum RecommendationJobStatus { COMPLETED = "COMPLETED" , FAILED = "FAILED" , IN_PROGRESS = "IN_PROGRESS" , PENDING = "PENDING" , STOPPED = "STOPPED" , STOPPING = "STOPPING" } export interface DescribeInferenceRecommendationsJobResponse { /**
     * <p>The name of the job. The name must be unique within an
     *            Amazon Web Services Region in the Amazon Web Services account.</p>
     */ JobName : string | undefined ; /**
     * <p>The job description that you provided when you initiated the job.</p>
     */ JobDescription ? : string ; /**
     * <p>The job type that you provided when you initiated the job.</p>
     */ JobType : RecommendationJobType | string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the job.</p>
     */ JobArn : string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the Amazon Web Services
     *            Identity and Access Management (IAM) role you provided when you initiated the job.</p>
     */ RoleArn : string | undefined ; /**
     * <p>The status of the job.</p>
     */ Status : RecommendationJobStatus | string | undefined ; /**
     * <p>A timestamp that shows when the job was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>A timestamp that shows when the job completed.</p>
     */ CompletionTime ? : Date ; /**
     * <p>A timestamp that shows when the job was last modified.</p>
     */ LastModifiedTime : Date | undefined ; /**
     * <p>If the job fails, provides information why the job failed.</p>
     */ FailureReason ? : string ; /**
     * <p>Returns information about the versioned model package Amazon Resource Name (ARN),
     *     the traffic pattern, and endpoint configurations you provided when you initiated the job.</p>
     */ InputConfig : RecommendationJobInputConfig | undefined ; /**
     * <p>The stopping conditions that you provided when you initiated the job.</p>
     */ StoppingConditions ? : RecommendationJobStoppingConditions ; /**
     * <p>The recommendations made by Inference Recommender.</p>
     */ InferenceRecommendations ? : InferenceRecommendation [ ] ; } export namespace DescribeInferenceRecommendationsJobResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeLabelingJobRequest { /**
     * <p>The name of the labeling job to return information for.</p>
     */ LabelingJobName : string | undefined ; } export namespace DescribeLabelingJobRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Provides a breakdown of the number of objects labeled.</p>
 */ export interface LabelCounters { /**
     * <p>The total number of objects labeled.</p>
     */ TotalLabeled ? : number ; /**
     * <p>The total number of objects labeled by a human worker.</p>
     */ HumanLabeled ? : number ; /**
     * <p>The total number of objects labeled by automated data labeling.</p>
     */ MachineLabeled ? : number ; /**
     * <p>The total number of objects that could not be labeled due to an error.</p>
     */ FailedNonRetryableError ? : number ; /**
     * <p>The total number of objects not yet labeled.</p>
     */ Unlabeled ? : number ; } export namespace LabelCounters { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } /**
 * <p>Specifies the location of the output produced by the labeling job. </p>
 */ export interface LabelingJobOutput { /**
     * <p>The Amazon S3 bucket location of the manifest file for labeled data. </p>
     */ OutputDatasetS3Uri : string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) for the most recent Amazon SageMaker model trained as part of
     *             automated data labeling. </p>
     */ FinalActiveLearningModelArn ? : string ; } export namespace LabelingJobOutput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export enum LabelingJobStatus { COMPLETED = "Completed" , FAILED = "Failed" , INITIALIZING = "Initializing" , IN_PROGRESS = "InProgress" , STOPPED = "Stopped" , STOPPING = "Stopping" } export interface DescribeLabelingJobResponse { /**
     * <p>The processing status of the labeling job. </p>
     */ LabelingJobStatus : LabelingJobStatus | string | undefined ; /**
     * <p>Provides a breakdown of the number of data objects labeled by humans, the number of
     *             objects labeled by machine, the number of objects than couldn't be labeled, and the
     *             total number of objects labeled. </p>
     */ LabelCounters : LabelCounters | undefined ; /**
     * <p>If the job failed, the reason that it failed. </p>
     */ FailureReason ? : string ; /**
     * <p>The date and time that the labeling job was created.</p>
     */ CreationTime : Date | undefined ; /**
     * <p>The date and time that the labeling job was last updated.</p>
     */ LastModifiedTime : Date | undefined ; /**
     * <p>A unique identifier for work done as part of a labeling job.</p>
     */ JobReferenceCode : string | undefined ; /**
     * <p>The name assigned to the labeling job when it was created.</p>
     */ LabelingJobName : string | undefined ; /**
     * <p>The Amazon Resource Name (ARN) of the labeling job.</p>
     */ LabelingJobArn : string | undefined ; /**
     * <p>The attribute used as the label in the output manifest file.</p>
     */ LabelAttributeName ? : string ; /**
     * <p>Input configuration information for the labeling job, such as the Amazon S3 location of the
     *             data objects and the location of the manifest file that describes the data
     *             objects.</p>
     */ InputConfig : LabelingJobInputConfig | undefined ; /**
     * <p>The location of the job's output data and the Amazon Web Services Key Management Service key ID for the key used to
     *             encrypt the output data, if any.</p>
     */ OutputConfig : LabelingJobOutputConfig | undefined ; /**
     * <p>The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf
     *             during data labeling.</p>
     */ RoleArn : string | undefined ; /**
     * <p>The S3 location of the JSON file that defines the categories used to label data
     *             objects. Please note the following label-category limits:</p>
     *         <ul>
     *             <li>
     *                 <p>Semantic segmentation labeling jobs using automated labeling: 20 labels</p>
     *             </li>
     *             <li>
     *                 <p>Box bounding labeling jobs (all): 10 labels</p>
     *             </li>
     *          </ul>
     *         <p>The file is a JSON structure in the following format:</p>
     *         <p>
     *             <code>{</code>
     *         </p>
     *         <p>
     *             <code> "document-version": "2018-11-28"</code>
     *         </p>
     *         <p>
     *             <code> "labels": [</code>
     *         </p>
     *         <p>
     *             <code> {</code>
     *         </p>
     *         <p>
     *             <code> "label": "<i>label 1</i>"</code>
     *         </p>
     *         <p>
     *             <code> },</code>
     *         </p>
     *         <p>
     *             <code> {</code>
     *         </p>
     *         <p>
     *             <code> "label": "<i>label 2</i>"</code>
     *         </p>
     *         <p>
     *             <code> },</code>
     *         </p>
     *         <p>
     *             <code> ...</code>
     *         </p>
     *         <p>
     *             <code> {</code>
     *         </p>
     *         <p>
     *             <code> "label": "<i>label n</i>"</code>
     *         </p>
     *         <p>
     *             <code> }</code>
     *         </p>
     *         <p>
     *             <code> ]</code>
     *         </p>
     *         <p>
     *             <code>}</code>
     *         </p>
     */ LabelCategoryConfigS3Uri ? : string ; /**
     * <p>A set of conditions for stopping a labeling job. If any of the conditions are met, the
     *             job is automatically stopped.</p>
     */ StoppingConditions ? : LabelingJobStoppingConditions ; /**
     * <p>Configuration information for automated data labeling.</p>
     */ LabelingJobAlgorithmsConfig ? : LabelingJobAlgorithmsConfig ; /**
     * <p>Configuration information required for human workers to complete a labeling
     *             task.</p>
     */ HumanTaskConfig : HumanTaskConfig | undefined ; /**
     * <p>An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in
     *             different ways, for example, by purpose, owner, or environment. For more information,
     *             see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services
     *                 Resources</a>.</p>
     */ Tags ? : Tag [ ] ; /**
     * <p>The location of the output produced by the labeling job.</p>
     */ LabelingJobOutput ? : LabelingJobOutput ; } export namespace DescribeLabelingJobResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeLineageGroupRequest { /**
     * <p>The name of the lineage group.</p>
     */ LineageGroupName : string | undefined ; } export namespace DescribeLineageGroupRequest { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeLineageGroupResponse { /**
     * <p>The name of the lineage group.</p>
     */ LineageGroupName ? : string ; /**
     * <p>The Amazon Resource Name (ARN) of the lineage group.</p>
     */ LineageGroupArn ? : string ; /**
     * <p>The display name of the lineage group.</p>
     */ DisplayName ? : string ; /**
     * <p>The description of the lineage group.</p>
     */ Description ? : string ; /**
     * <p>The creation time of lineage group.</p>
     */ CreationTime ? : Date ; /**
     * <p>Information about the user who created or modified an experiment, trial, trial
     *       component, lineage group, or project.</p>
     */ CreatedBy ? : UserContext ; /**
     * <p>The last modified time of the lineage group.</p>
     */ LastModifiedTime ? : Date ; /**
     * <p>Information about the user who created or modified an experiment, trial, trial
     *       component, lineage group, or project.</p>
     */ LastModifiedBy ? : UserContext ; } export namespace DescribeLineageGroupResponse { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; } export interface DescribeModelInput { /**
     * <p>The name of the model.</p>
     */ ModelName : string | undefined ; } export namespace DescribeModelInput { internal export const filterSensitiveLog = ( obj ) : any => ( { ... obj , } ) ; }