Definition: A model registry is a central catalog that stores and tracks AI models, their versions, metadata, evaluation results and lifecycle status.Why It Matters: Without a registry, teams lose track of which model is deployed, how it was trained and who approved it. A registry makes deployment repeatable and audits possible, and it supports rollback. It is a foundation of model governance.Key Characteristics: A registry records model artifacts, version numbers, training data references, metrics, owners, licenses and stages such as staging, production and archived. It integrates with CI/CD, deployment and monitoring, and enforces approval workflows.
After training or selecting a model, a team registers it with its artifacts and metadata, including the evaluation results. Reviewers approve or reject it, and the stage is updated. Deployment systems pull the approved version from the registry by name and version. When a new version is promoted, the previous one is kept so rollback is possible. Monitoring links production behavior back to the registered version. Access controls decide who can register, approve and deploy.ProsA registry gives traceability, repeatable deployment and a single source of truth.ConsIt requires discipline to keep updated, and metadata quality varies.
Controlled Deployment: Only models marked as approved can be deployed to production.Audit Support: A regulated firm shows which model version produced a past decision.Rollback: A team reverts to the previous version when a new model underperforms.
Ad Hoc Storage (2000s-2010s): Models were saved as files in shared folders.MLOps Tools (2018-2020): Platforms introduced registries with versioning and stages.LLM Era (2023): Registries expanded to cover prompts, adapters and third-party models.Current Practice and Trends (2024-Present): Registries integrate governance, licensing and policy checks.
When to Use: Adopt a registry once more than a few models are in use, or when governance and audit matter.Designing for Reliability: Require registration for deployment, and keep previous versions for rollback.Operating at Scale: Automate registration from pipelines and review stale entries regularly.Governance and Risk: Control approval rights, record licenses and data lineage, and retain history.
