Central repository for storing, versioning, and managing trained machine learning models with their metadata. Like a library catalog system that tracks all versions of AI models.
A model registry stores each version of a recommendation model, tracking which version is in production, staging, or archived.
All four provide a centralized place to register trained models, track versions and metadata, and support promotion through environments (e.g., staging to production). Feature depth varies by platform, but the core goal—governed model lifecycle management—is the same.