Model Registries & Artifact Integrity

AI systems depend on more than models alone. Organizations must manage datasets, trained models, configurations, dependencies, documentation, and deployment packages throughout the AI lifecycle. This lesson explores model registries and artifact integrity, examining how organizations maintain visibility, traceability, provenance, and trust in AI assets. Learners will study model registries, artifact management practices, version control, lineage tracking, cryptographic integrity controls, and reproducibility requirements. Understanding artifact governance is essential for AI governance auditors because accountability, compliance, and assurance depend on the ability to identify, verify, and trace AI assets throughout their lifecycle.