AWS SDK Version 3 for .NET
API Reference

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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:

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.

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 ManifestS3Uri 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 (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.

Note:

For .NET Core this operation is only available in asynchronous form. Please refer to CreateLabelingJobAsync.

Namespace: Amazon.SageMaker
Assembly: AWSSDK.SageMaker.dll
Version: 3.x.y.z

Syntax

C#
public virtual CreateLabelingJobResponse CreateLabelingJob(
         CreateLabelingJobRequest request
)
Parameters
request
Type: Amazon.SageMaker.Model.CreateLabelingJobRequest

Container for the necessary parameters to execute the CreateLabelingJob service method.

Return Value
The response from the CreateLabelingJob service method, as returned by SageMaker.

Exceptions

ExceptionCondition
ResourceInUseException Resource being accessed is in use.
ResourceLimitExceededException You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Version Information

.NET Framework:
Supported in: 4.5, 4.0, 3.5

See Also