Question

A financial services company has a central ML platform team in one AWS account that trains and approves models, and several business-unit teams in separate AWS accounts that deploy those models. Compliance requires that only approved model versions be deployable, that model metadata and approval status be centrally tracked, and that teams cannot deploy unapproved models. Which approach best meets these governance requirements?

Accepted Answer

Use a shared Amazon SageMaker Model Registry with model package groups, register models with an approval status, and grant business-unit accounts read/deploy access only to Approved model versions via resource policies and IAM.

Answer

Copy approved model artifacts into an S3 bucket in each business-unit account and let each team deploy directly from their local copy without central tracking.

Answer

Give every business-unit account full IAM access to the central training account so they can pick whichever model they want to deploy.

Answer

Email the approved model artifact files to each business-unit team and rely on a manual checklist to confirm they deploy the correct version.