Model Registry

What this topic covers

• Foundations — A model registry is the often-overlooked bridge between training and production: it enforces that every deployed model is traceable to a specific, versioned artifact — not just an ad-hoc file or a vague runtime tag.
• Implementation — The guides here cover registry integration end to end: registering and staging model artifacts, configuring promotion gates, enforcing lifecycle policies, and wiring rollback into your deployment pipeline.
• What's changing — The registry concept is shifting from ML-model-centric to LLM-weight-centric — adapting to multi-cloud environments, foundation model versioning, and governance demands that didn't exist when the first registries were designed.
• Risks & limits — A registry makes deployment look controlled, but automated promotion pipelines can widen accountability gaps — the audit trail records what was deployed, not whether anyone checked if it should have been.