aws-java-sdk-sagemaker/src/main/java/com/amazonaws/services/sagemaker/AmazonSageMakerClient.java
@Override
    public CreateTransformJobResult createTransformJob(CreateTransformJobRequest request) {
        request = beforeClientExecution(request);
        return executeCreateTransformJob(request);
    }
/**
     * <p>
     * Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these
     * results to an Amazon S3 location that you specify.
     * </p>
     * <p>
     * To perform batch transformations, you create a transform job and use the data that you have readily available.
     * </p>
     * <p>
     * In the request body, you provide the following:
     * </p>
     * <ul>
     * <li>
     * <p>
     * <code>TransformJobName</code> - Identifies the transform job. The name must be unique within an AWS Region in an
     * AWS account.
     * </p>
     * </li>
     * <li>
     * <p>
     * <code>ModelName</code> - Identifies the model to use. <code>ModelName</code> must be the name of an existing
     * Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see
     * <a>CreateModel</a>.
     * </p>
     * </li>
     * <li>
     * <p>
     * <code>TransformInput</code> - Describes the dataset to be transformed and the Amazon S3 location where it is
     * stored.
     * </p>
     * </li>
     * <li>
     * <p>
     * <code>TransformOutput</code> - Identifies the Amazon S3 location where you want Amazon SageMaker to save the
     * results from the transform job.
     * </p>
     * </li>
     * <li>
     * <p>
     * <code>TransformResources</code> - Identifies the ML compute instances for the transform job.
     * </p>
     * </li>
     * </ul>
     * <p>
     * For more information about how batch transformation works Amazon SageMaker, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html">How It Works</a>.
     * </p>
     * 
     * @param createTransformJobRequest
     * @return Result of the CreateTransformJob operation returned by the service.
     * @throws ResourceInUseException
     *         Resource being accessed is in use.
     * @throws ResourceLimitExceededException
     *         You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
     *         created.
     * @sample AmazonSageMaker.CreateTransformJob
     * @see <a href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateTransformJob" target="_top">AWS
     *      API Documentation</a>
     */

Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these
results to an Amazon S3 location that you specify.


To perform batch transformations, you create a transform job and use the data that you have readily available.


In the request body, you provide the following:




TransformJobName - Identifies the transform job. The name must be unique within an AWS Region in an
AWS account.




ModelName - Identifies the model to use. ModelName must be the name of an existing
Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see
CreateModel.




TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is
stored.




TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the
results from the transform job.




TransformResources - Identifies the ML compute instances for the transform job.




For more information about how batch transformation works Amazon SageMaker, see How It Works.

@param createTransformJobRequest
@return Result of the CreateTransformJob operation returned by the service.
@throws ResourceInUseException
Resource being accessed is in use.
@throws ResourceLimitExceededException
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.
Starts a transform job . A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify . To perform batch transformations you create a transform job and use the data that you have readily available . In the request body you provide the following : TransformJobName - Identifies the transform job . The name must be unique within an AWS Region in an AWS account . ModelName - Identifies the model to use . ModelName must be the name of an existing Amazon SageMaker model in the same AWS Region and AWS account . For information on creating a model see CreateModel . TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored . TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job . TransformResources - Identifies the ML compute instances for the transform job . For more information about how batch transformation works Amazon SageMaker see How It Works . @param createTransformJobRequest @return Result of the CreateTransformJob operation returned by the service . @throws ResourceInUseException Resource being accessed is in use . @throws ResourceLimitExceededException You have exceeded an Amazon SageMaker resource limit . For example you might have too many training jobs created .
@Override
    public CreateTransformJobResult createTransformJob(CreateTransformJobRequest request) {
        request = beforeClientExecution(request);
        return executeCreateTransformJob(request);
    }
@ Override public CreateTransformJobResult createTransformJob ( CreateTransformJobRequest request ) { request = beforeClientExecution ( request ) ; return executeCreateTransformJob ( request ) ; }
<p>
 Starts a transform job.
<p > Starts a transform job .