code: @Override
      public CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest request) {
          request = beforeClientExecution(request);
          return executeCreateEndpointConfig(request);
      }
docs: <p>
  Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the
  configuration, you identify one or more models, created using the <code>CreateModel</code> API, to deploy and the
  resources that you want Amazon SageMaker to provision. Then you call the <a
  href="https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpoint.html">CreateEndpoint</a> API.
  </p>
  <note>
  <p>
  Use this API only if you want to use Amazon SageMaker hosting services to deploy models into production.
  </p>
  </note>
  <p>
  In the request, you define one or more <code>ProductionVariant</code>s, each of which identifies a model. Each
  <code>ProductionVariant</code> parameter also describes the resources that you want Amazon SageMaker to
  provision. This includes the number and type of ML compute instances to deploy.
  </p>
  <p>
  If you are hosting multiple models, you also assign a <code>VariantWeight</code> to specify how much traffic you
  want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign
  traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model
  A, and one-third to model B.
  </p>
  
  @param createEndpointConfigRequest
  @return Result of the CreateEndpointConfig operation returned by the service.
  @throws ResourceLimitExceededException
  You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
  created.
  @sample AmazonSageMaker.CreateEndpointConfig
  @see <a href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateEndpointConfig" target="_top">AWS
  API Documentation</a>