Manage Registered Models - Amazon SageMaker Unified Studio
Model lifecycle management

Manage Registered Models

Your registered models can be accessed and managed in Amazon SageMaker Unified Studio.

  1. Navigate to AI/ML > Models and select the Registered Models tab to view the models.

  2. You can review the following information

    • Model group name - Logical grouping for related model versions

    • Model version - Specific version identifier

    • Model artifacts - Location of model files in Amazon S3

    • Description - Optional description of the model and its purpose

  3. Select specific models to review key model information like

    • Framework - Machine learning framework used (e.g., PyTorch, TensorFlow)

    • Algorithm - Algorithm or approach used for training

    • Performance metrics - Accuracy, precision, recall, or other relevant metrics

Model lifecycle management

The model registry provides version control and lifecycle management:

  • Version tracking - Each model registration creates a new version with unique metadata

  • Approval workflows - Models can have approval status (Approved, Pending, Rejected)

  • Deployment status - Track which versions are deployed to endpoints

  • Model comparison - Compare metrics and metadata across versions

To manage model versions:

  1. Choose a model group name to view all versions.

  2. Review version details including:

    • Version number - Sequential version identifier

    • Description - Version-specific notes and changes

    • Deployment Status - Current deployment state

    • Approval Status - Workflow approval state

    • Modified Date - Last update timestamp

  3. Use the Actions dropdown to:

    • Deploy using Notebooks - Use a pre-created notebook to deploy the model to SageMaker AI Endpoint

    • Deploy using Jupyter Notebook - Use a pre-created JupyterLab notebook to deploy the model to SageMaker AI Endpoint

    • Approve/Reject - Update approval status

    • Delete - Delete the selected version