<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 Amazon Web Services 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="https://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="https://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> <p>You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in <code>ManifestS3Uri</code> have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (<code>InProgress</code>) streaming labeling job in real time. To learn how to create a static labeling job, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-create-labeling-job-api.html">Create a Labeling Job (API) </a> in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-streaming-create-job.html">Create a Streaming Labeling Job</a>.</p>

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    "id": "abc123",
    "created_at": "2025-01-01T00:00:00Z"
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<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 Amazon Web Services 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="https://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="https://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> <p>You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in <code>ManifestS3Uri</code> have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (<code>InProgress</code>) streaming labeling job in real time. To learn how to create a static labeling job, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-create-labeling-job-api.html">Create a Labeling Job (API) </a> in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sms-streaming-create-job.html">Create a Streaming Labeling Job</a>.</p>

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    "created_at": "2025-01-01T00:00:00Z"
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  "success": false,
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    "code": "VALIDATION_ERROR",
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