Creating a lookalike segment - AWS Clean Rooms

Creating a lookalike segment

Note

You can only supply a training data set for using in a Clean Rooms ML lookalike model that has data stored in Amazon S3. However, you can supply the seed data for a lookalike model using SQL that runs across data stored in any supported data source.

A lookalike segment is a subset of the training data that most closely resembles the seed data.

To create a lookalike segment in AWS Clean Rooms

  1. Sign in to the AWS Management Console and open the AWS Clean Rooms console with your AWS account (if you haven't yet done so).

  2. In the left navigation pane, choose Collaborations.

  3. On the With active membership tab, choose a collaboration.

  4. On the ML Models tab, choose Create lookalike segment.

  5. On the Create lookalike segment page, for Associated configured lookalike model, choose the associated configured lookalike model to use for this lookalike segment.

  6. For Lookalike segment details enter a Name and optional Description.

  7. For Seed profiles, choose your Seed method by selecting an option and then taking the recommended action.

    Option Recommended action
    Amazon S3 path

    1. Select an Amazon S3 location.

    2. (Optional) Choose Include seed profiles in the output.
    SQL query Write a SQL query and use its results as the seed data.
    Analysis template Choose an analysis template from the dropdown list and use the results created by an analysis template.

  8. Choose the Worker type and Number of workers to use when creating this data channel.

  9. For Service access, choose the Existing service role name that will be used to access this table.

  10. If you want to enable Tags for the training dataset, choose Add new tag and then enter the Key and Value pair.

  11. Choose Create lookalike segment.

For the corresponding API action, see StartAudienceGenerationJob.