Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

This blog post discusses how to centralize model governance with SageMaker Model Registry Resource Access Manager sharing, enabling organizations to securely share and access machine learning (ML) models across AWS accounts.

Specifically, the article covers:

• Use case and model lifecycle stages, including stages like development, quality assurance, pre-production, and production
• Multi-account architecture for sharing models using SageMaker Model Registry and AWS Resource Access Manager (RAM)
• Building a central model registry by creating and sharing a model group across accounts using the SageMaker Studio UI and APIs
• Using MLflow for experimentation with the shared model group, including tracking experiments, training models, and registering models
• Design considerations for use case and model stage governance, tracking attributes like model stage, status, metrics, etc.
• Deployment and governance workflows, covering model approval, deployment, validation, and monitoring across stages