Model Versioning & Registries

What it is

A model registry tracks every trained model artifact along with its metadata — training data version, code version, metrics, and lineage — making it possible to reproduce, compare, and roll back models reliably.

Key points

• Reproducibility: without versioning the training data and code alongside the model artifact, "why did this model behave differently from last month's" becomes nearly impossible to answer.
• Stage transitions: registries typically track a model's lifecycle stage (staging, production, archived) — giving an explicit, auditable gate before a model reaches production traffic.
• Metadata beyond the weights: a registry entry includes training metrics, the dataset version, and hyperparameters — the model file alone, without this context, isn't enough to debug a production issue later.
• Registries are what makes rollback to a previous model version a routine, low-risk operation instead of a scramble to reconstruct what was previously deployed.