aws-java-sdk-sagemaker/src/main/java/com/amazonaws/services/sagemaker/AmazonSageMakerClient.java
@Override
    public CreateLabelingJobResult createLabelingJob(CreateLabelingJobRequest request) {
        request = beforeClientExecution(request);
        return executeCreateLabelingJob(request);
    }
/**
     * <p>
     * Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to
     * train machine learning models.
     * </p>
     * <p>
     * You can select your workforce from one of three providers:
     * </p>
     * <ul>
     * <li>
     * <p>
     * A private workforce that you create. It can include employees, contractors, and outside experts. Use a private
     * workforce when want the data to stay within your organization or when a specific set of skills is required.
     * </p>
     * </li>
     * <li>
     * <p>
     * One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas.
     * </p>
     * </li>
     * <li>
     * <p>
     * The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data
     * or data that has been stripped of any personally identifiable information.
     * </p>
     * </li>
     * </ul>
     * <p>
     * You can also use <i>automated data labeling</i> to reduce the number of data objects that need to be labeled by a
     * human. Automated data labeling uses <i>active learning</i> to determine if a data object can be labeled by
     * machine or if it needs to be sent to a human worker. For more information, see <a
     * href="http://docs.aws.amazon.com/sagemaker/latest/dg/sms-automated-labeling.html">Using Automated Data
     * Labeling</a>.
     * </p>
     * <p>
     * The data objects to be labeled are contained in an Amazon S3 bucket. You create a <i>manifest file</i> that
     * describes the location of each object. For more information, see <a
     * href="http://docs.aws.amazon.com/sagemaker/latest/dg/sms-data.html">Using Input and Output Data</a>.
     * </p>
     * <p>
     * The output can be used as the manifest file for another labeling job or as training data for your machine
     * learning models.
     * </p>
     * 
     * @param createLabelingJobRequest
     * @return Result of the CreateLabelingJob 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.CreateLabelingJob
     * @see <a href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateLabelingJob" target="_top">AWS
     *      API Documentation</a>
     */

Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to
train machine learning models.


You can select your workforce from one of three providers:




A private workforce that you create. It can include employees, contractors, and outside experts. Use a private
workforce when want the data to stay within your organization or when a specific set of skills is required.




One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas.




The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data
or data that has been stripped of any personally identifiable information.




You can also use automated data labeling to reduce the number of data objects that need to be labeled by a
human. Automated data labeling uses active learning to determine if a data object can be labeled by
machine or if it needs to be sent to a human worker. For more information, see Using Automated Data
Labeling.


The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that
describes the location of each object. For more information, see Using Input and Output Data.


The output can be used as the manifest file for another labeling job or as training data for your machine
learning models.

@param createLabelingJobRequest
@return Result of the CreateLabelingJob 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.
Creates a job that uses workers to label the data objects in your input dataset . You can use the labeled data to train machine learning models . You can select your workforce from one of three providers : A private workforce that you create . It can include employees contractors and outside experts . Use a private workforce when want the data to stay within your organization or when a specific set of skills is required . One or more vendors that you select from the AWS Marketplace . Vendors provide expertise in specific areas . The Amazon Mechanical Turk workforce . This is the largest workforce but it should only be used for public data or data that has been stripped of any personally identifiable information . You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human . Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker . For more information see Using Automated Data Labeling . The data objects to be labeled are contained in an Amazon S3 bucket . You create a manifest file that describes the location of each object . For more information see Using Input and Output Data . The output can be used as the manifest file for another labeling job or as training data for your machine learning models . @param createLabelingJobRequest @return Result of the CreateLabelingJob 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 CreateLabelingJobResult createLabelingJob(CreateLabelingJobRequest request) {
        request = beforeClientExecution(request);
        return executeCreateLabelingJob(request);
    }
@ Override public CreateLabelingJobResult createLabelingJob ( CreateLabelingJobRequest request ) { request = beforeClientExecution ( request ) ; return executeCreateLabelingJob ( request ) ; }
<p>
 Creates a job that uses workers to label the data objects in your input dataset.
<p > Creates a job that uses workers to label the data objects in your input dataset .