Processing data in a custom labeling workflow with AWS Lambda - Amazon SageMaker AI

Processing data in a custom labeling workflow with AWS Lambda

In this topic, you can learn how to deploy optional AWS Lambda functions when creating a custom labeling workflow. You can specify two types of Lambda functions to use with your custom labeling workflow.

  • Pre-annotation Lambda: This function pre-processes each data object sent to your labeling job prior to sending it to workers.

  • Post-annotation Lambda: This function processes the results once workers submit a task. If you specify multiple workers per data object, this function may include logic to consolidate annotations.

If you are a new user of Lambda and Ground Truth, we recommend that you use the pages in this section as follows:

  1. First, review Using pre-annotation and post-annotation Lambda functions.

  2. Then, use the page Add required permissions to use AWS Lambda with Ground Truth to learn about security and permission requirements to use your pre-annotation and post-annotation Lambda functions in a Ground Truth custom labeling job.

  3. Next, you need to visit the Lambda console or use Lambda's APIs to create your functions. Use the section Create Lambda functions using Ground Truth templates to learn how to create Lambda functions.

  4. To learn how to test your Lambda functions, see Test pre-annotation and post-annotation Lambda functions.

  5. After you create pre-processing and post-processing Lambda functions, select them from the Lambda functions section that comes after the code editor for your custom HTML in the Ground Truth console. To learn how to use these functions in a CreateLabelingJob API request, see Create a Labeling Job (API).

For a custom labeling workflow tutorial that includes example pre-annotation and post-annotation Lambda functions, see Demo template: Annotation of images with crowd-bounding-box.

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