The model registry is the central repository for trained model artefacts, serving the same role for models that the code repository serves for source code. Four tooling options are recommended: MLflow Model Registry, Weights & Biases Model Registry, Amazon SageMaker Model Registry, and Vertex AI Model Registry.

The choice between these tools should be informed by the organisation’s existing infrastructure, the level of integration with the CI/CD pipeline, and the registry’s support for immutable versioning and access control. MLflow is open-source and vendor-neutral, making it suitable for organisations that need flexibility. Weights & Biases offers strong experiment tracking integration. SageMaker and Vertex AI integrate deeply with their respective cloud ecosystems.

Regardless of the tool selected, the model registry must support six capabilities: immutable versioning, metadata attachment, lineage tracking, stage management, access control, and long-term retrieval. Organisations that self-host their registry should ensure the underlying storage meets the durability and availability requirements for a compliance-critical artefact store. The registry’s contents, specifically the metadata for each production model version, are themselves evidence artefacts for the conformity assessment evidence pack.

Key outputs

• Selected model registry tool with deployment configuration
• Verification that all six required capabilities are supported
• Module 3 and Module 10 AISDP documentation