Model Registry, Lineage, and Governance

With the best model identified and experiment metadata captured in SageMaker Experiments, the next operational challenge is moving that model into production through a controlled, auditable process. This is where the AWS Certified Machine Learning Engineer  Associate exam tests your understanding of governance tooling. Teams routinely accumulate dozens of model versions across experiments, Autopilot runs, and manual tuning jobs. Without a centralized registry, there is no reliable mechanism to enforce approval gates, trace which dataset or training job produced a given artifact, or satisfy compliance audits. This lesson covers three AWS features that solve these problems: SageMaker Model Registry for versioning and approval workflows, Model Lineage Tracking for artifact provenance, and Model Cards for structured governance documentation. Exam questions about model versioning, deployment gating, and audit trails consistently point to these services rather than custom DynamoDB tracking tables or S3 naming conventions.

SageMaker Model Registry fundamentals

SageMaker Model Registry functions as a centralized catalog that stores model versions within named logical collections. Think of it as a library catalog for your trained models, where every edition of every book is tracked, compared, and checked out through a formal process.

Core concepts and registration flow

The registry organizes models using two primary abstractions. A Model Package GroupA named logical collection that groups all versions of a particular ML model, such as fraud-detection-models or credit-scoring-v2. acts as the top-level container. Each individual version within that group is a model package.

Model versions are registered through two paths:

• Programmatic registration: A data scientist calls create_model_package via the SageMaker SDK, specifying the artifact location, container image, ...