Custom modeling in AWS Clean Rooms ML - AWS Clean Rooms

Custom modeling in AWS Clean Rooms ML

From a technical standpoint, the following diagram describes how custom ML modeling works in AWS Clean Rooms ML.

An overview of how AWS Clean Rooms ML works with custom models.

Here's how custom ML modeling works in Clean Rooms ML:

  1. Data Source Configuration

    • Source data can be stored in Amazon S3 catalog, in the AWS Glue Data Catalog, or Snowflake

    • AWS Glue Data Catalog is used to organize and catalog

    • Data from multiple AWS accounts can be used within the same collaboration

  2. SQL Query and Data Processing

    • SQL queries are used to access and process the source data

    • The queries run within the AWS Clean Rooms collaboration boundaries

    • Processed data feeds into ML Input Channels for model training

  3. ML Model Development

    • Source code for the model can be developed using AWS Deep Learning Container Images

    • Custom container images must be created and stored in Amazon Elastic Container Registry

  4. Infrastructure Components

    • Amazon Elastic Container Registry stores and manages the ML model containers

    • ML processing occurs within the secure AWS Clean Rooms collaboration environment

  5. Monitoring and Logging

    • Amazon CloudWatch provides metrics and logs for both collaborating parties

    • Monitoring is available across AWS accounts involved in the collaboration

    • Performance metrics and operational logs are accessible to relevant parties

  6. Results Management

    • Access to results is controlled according to collaboration permissions

Before you get started, see the Custom ML modeling prerequisites and Model authoring guidelines for the training container for more information.

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