A model registry is the catalogue of trained models that are candidates for production or already in it. Each entry holds an immutable version, the artifact, its metrics, its stage, and a pointer back to the run that produced it.
Versions matter because a model is a moving binary with no compile-time contract. Two files named fraud_model.pkl can behave completely differently, and neither will complain. Without a version stamped into the serving logs, you cannot say which model produced a specific bad prediction.
The registry also gives deployment something stable to point at. The service asks for the production alias, not a file path on someone's laptop or a bucket key that got overwritten last week. That one layer of indirection turns rollback into a config change instead of a rebuild. It usually pays for itself during the first bad release.
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