Each model version in the registry must carry structured metadata that links it to every artefact in its provenance chain. This metadata transforms the registry from a simple artefact store into the navigable index that makes end-to-end traceability practical.

The minimum metadata set includes the training dataset version (DVC, Delta Lake, or LakeFS reference), the training code version (Git commit SHA), the pipeline execution ID (identifying the specific CI/CD run that built and tested the model), the content hash (SHA-256 of the model artefact for integrity verification), the hyperparameter configuration, the full set of performance and fairness metrics from the validation gates, and the approval status.

A reference MLflow implementation is provided below demonstrating how these metadata fields are attached as model version tags. Each tag uses a namespaced key (e.g. aisdp.training_data_version, aisdp.code_commit, aisdp.content_hash) to distinguish compliance metadata from operational metadata. The metadata attachment should be automated as part of the CI/CD pipeline’s model registration step, ensuring that no model enters the registry without its complete provenance record.

Key outputs

• Metadata schema defining all required fields per model version
• Automated metadata attachment in the CI/CD registration step
• Validation that all required fields are populated before registration completes
• Module 3 and Module 10 AISDP documentation