Amazon SageMaker Model Registry now supports defining machine learning model lifecycle stages
Today, we are excited to announce that Amazon SageMaker Model Registry now supports custom machine learning (ML) model lifecycle stages. This capability further improves model governance by enabling data scientists and ML engineers to define and control the progression of their models across various stages, from development to production.
Customers use Amazon SageMaker Model Registry as a purpose-built metadata store to manage the entire lifecycle of ML models. With this launch, data scientists and ML engineers can now define custom stages such as development, testing, and production for ML models in the model registry. This makes it easy to track and manage models as they transition across different stages in the model lifecycle from training to inference. They can also track stage approval status such as Pending Approval, Approved, and Rejected to check when the model is ready to move to the next stage. These custom stages and approval status help data scientists and ML engineers define and enforce model approval workflows, ensuring that models meet specific criteria before advancing to the next stage. By implementing these custom stages and approval processes, customers can standardize their model governance practices across their organization, maintain better oversight of model progression, and ensure that only approved models reach production environments.
This capability is available in all AWS regions where Amazon SageMaker Model Registry is currently available except GovCloud regions. To learn more, see Staging Construct for your Model Lifecycle.