Rolling back a model should be simple. In many systems, it is not.

Without versioning, teams end up with loosely named artifacts and no reliable answers to basic questions: Which model is serving traffic? What data trained it? Which feature schema does it expect? Which previous version is safe to roll back to?

This is what model versioning fixes.

A model registry assigns a clear version to each deployable artifact, stores the metadata needed to understand it, and exposes stable references such as champion, candidate, or production. With that in place, rollback becomes a pointer change backed by validation, not a manual search through object storage.