Amazon SageMaker Model Registry now supports defining machine learning model lifecycle stages

Amazon SageMaker Model Registry now supports defining custom machine learning (ML) model lifecycle stages, allowing data scientists and ML engineers to better govern and control the progression of models from development to production.

Specifically, the article covers:

• Data scientists and ML engineers can define custom stages such as development, testing, and production for ML models in the Model Registry.
• They can track stage approval status (Pending Approval, Approved, Rejected) to ensure models meet specific criteria before advancing to the next stage.
• Custom stages and approval workflows help standardize model governance practices across organizations and maintain oversight of model progression.
• This new capability enables implementing approval processes to ensure only approved models reach production environments.