An ML engineer has developed a binary classification model outside of Amazon SageMaker.

The SageMaker Canvas user needs permissions to access the Amazon S3 bucket where the model artifacts are stored to retrieve the model for use in Canvas.

Registering the model in the SageMaker Model Registry allows the model to be tracked and managed within the SageMaker ecosystem. This makes it accessible for tuning and deployment through SageMaker Canvas.

This combination ensures proper access control and integration within SageMaker, enabling the Canvas user to work with the model.