# Create a Labeling Job
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You can create a labeling job in the Amazon SageMaker AI console and by using an AWS SDK in your preferred language to run `CreateLabelingJob`. After a labeling job has been created, you can track worker metrics (for private workforces) and your labeling job status using [CloudWatch](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-monitor-cloud-watch.html).

Before you create a labeling job it is recommended that you review the following pages, as applicable:
+ You can specify your input data using an automatic data setup in the console, or an input manifest file in either the console or when using `CreateLabelingJob` API. For automated data setup, see [Automate data setup for labeling jobs](sms-console-create-manifest-file.md). To learn how to create an input manifest file, see [Input manifest files](sms-input-data-input-manifest.md).
+ Review labeling job input data quotas: [Input Data Quotas](input-data-limits.md).

After you have chosen your task type, use the topics on this page to learn how to create a labeling job.

If you are a new Ground Truth user, we recommend that you start by walking through the demo in [Getting started: Create a bounding box labeling job with Ground Truth](sms-getting-started.md).

**Important**  
Ground Truth requires all S3 buckets that contain labeling job input image data to have a CORS policy attached. To learn more, see [CORS Requirement for Input Image Data](sms-cors-update.md).

**Topics**
+ [Built-in Task Types](sms-task-types.md)
+ [Create instruction pages](sms-creating-instruction-pages.md)
+ [Create a Labeling Job (Console)](sms-create-labeling-job-console.md)
+ [Create a Labeling Job (API)](sms-create-labeling-job-api.md)
+ [Create a streaming labeling job](sms-streaming-create-job.md)
+ [Labeling category configuration file with label category and frame attributes reference](sms-label-cat-config-attributes.md)