Rolling back a model should be simple. In practice, it often isn’t.

Without proper versioning, teams end up with dozens of loosely named artifacts and no clear way to answer basic questions. Which model was in production? What data was it trained on? Is it compatible with the current feature pipeline?

This is what model versioning fixes.

A model registry assigns a clear version to every model, stores the metadata needed to understand and reproduce it, and tracks its lifecycle from staging to production. With that in place, rollback becomes a simple, reliable operation instead of a manual investigation.