There is a command group 'az_ml_batch endpoint'. This group of commands could 'manage azure ml batch endpoints'. The group of commands has global parameter '--debug' which means 'Increase logging verbosity to show all debug logs.'. The group of commands has global parameter '--help -h' which means 'Show this help message and exit.'. The group of commands has global parameter '--only-show-errors' which means 'Only show errors, suppressing warnings.'. The group of commands has global parameter '--output -o' which means 'Output format.'. The group of commands has global parameter '--query' which means 'JMESPath query string. See <a href="http://jmespath.org/">http://jmespath.org/</a> for more information and examples.'. The group of commands has global parameter '--subscription' which means 'Name or ID of subscription. You can configure the default subscription using `az account set -s NAME_OR_ID`.'. The group of commands has global parameter '--verbose' which means 'Increase logging verbosity. Use --debug for full debug logs.'. This group contains a child command 'az_ml_batch endpoint_invoke'.The syntax is: az ml batch-endpoint invoke --name
                            --resource-group
                            --workspace-name
                            [--deployment-name]
                            [--experiment-name]
                            [--file]
                            [--input]
                            [--input-type]
                            [--inputs]
                            [--instance-count]
                            [--job-name]
                            [--mini-batch-size]
                            [--output-path]
                            [--outputs]
                            [--set]. The 1th example of the child command would be to use it to 'invoke a batch endpoint with input data from a registered azure ml data asset and override default deployment setting for mini_batch_size', the syntax is: az ml batch-endpoint invoke --name my-batch-endpoint --input azureml:my-dataset:1 --mini-batch-size 64 --resource-group my-resource-group --workspace-name my-workspace. The 2th example of the child command would be to use it to 'invoke a batch endpoint with input file from a public uri', the syntax is: az ml batch-endpoint invoke --name my-batch-endpoint --input-type uri_file --input https://pipelinedata.blob.core.windows.net/sampledata/mnist/0.png --resource-group my-resource-group --workspace-name my-workspace. The 3th example of the child command would be to use it to 'invoke a batch endpoint with input file from a registered datastore', the syntax is: az ml batch-endpoint invoke --name my-batch-endpoint --input-type uri_file --input azureml://datastores/workspaceblobstore/paths/{path_to_data}/mnist/0.png --resource-group my-resource-group --workspace-name my-workspace. The 4th example of the child command would be to use it to 'invoke a batch endpoint with input folder from a public uri', the syntax is: az ml batch-endpoint invoke --name my-batch-endpoint --input-type uri_folder --input https://pipelinedata.blob.core.windows.net/sampledata/mnist --resource-group my-resource-group --workspace-name my-workspace. The 5th example of the child command would be to use it to 'invoke a batch endpoint with input folder from a registered datastore', the syntax is: az ml batch-endpoint invoke --name my-batch-endpoint --input-type uri_folder --input azureml://datastores/workspaceblobstore/paths/{path_to_data}/mnist --resource-group my-resource-group --workspace-name my-workspace. The 6th example of the child command would be to use it to 'invoke a batch endpoint with files in a local folder', the syntax is: az ml batch-endpoint invoke --name my-batch-endpoint --input ./mnist_folder --resource-group my-resource-group --workspace-name my-workspace. The 7th example of the child command would be to use it to 'invoke a batch endpoint with a local folder as the input and output path and overwrite some batch deployment settings during endpoint invoke', the syntax is: az ml batch-endpoint invoke --name my-batch-endpoint --input ./mnist_folder --instance-count 2 --mini-batch-size 5 --output-path azureml://datastores/workspaceblobstore/paths/tests/output --resource-group my-resource-group --workspace-name my-workspace. The parameter '--name -n' is required. It is used to name of the batch endpoint.. The parameter '--resource-group -g' is required. It is used to name of resource group. you can configure the default group using `az configure --defaults group=<name>`.. The parameter '--workspace-name -w' is required. It is used to name of the azure ml workspace. you can configure the default workspace using `az configure --defaults workspace=<name>`.. The parameter '--deployment-name -d' is optional. name of the deployment to target.. The parameter '--experiment-name' is optional. name of the experiment for pipeline component deployment.. The parameter '--file -f' is optional. name of the file used for batch invoke.. The parameter '--input' is optional. reference to input data to use for batch inferencing. it can be a path on the datastore, public uri, a registered data asset, or a local folder path.. The parameter '--input-type' is optional. type of the input, specifying whether it's a file or a folder. use this when you are using a path on datastore or public uri. supported values: uri_folder, uri_file.. The parameter '--inputs' is optional. dictionary of inputs of invoke jobs.. The parameter '--instance-count -c' is optional. number of instances the prediction will run on.. The parameter '--job-name' is optional. name of the job for batch invoke.. The parameter '--mini-batch-size -m' is optional. size of each mini batch that the input data will be split into for prediction.. The parameter '--output-path' is optional. path on the datastore where output files will be uploaded to.. The parameter '--outputs' is optional. dictionary to specify where to store the results.. The parameter '--set' is optional. update an object by specifying a property path and value to set. example: --set property1.property2=<value>.. 