ModelRefs / Model Registry — AI Glossary
Model Registry — AI Glossary
A centralized store for managing the lifecycle of trained models from experimentation through staging to production deployment. Three things get conflated.
Overview
Model registries manage model versions (each unique model artifact), stage transitions (None→Staging→Production→Archived), annotations (performance metrics, data version, approval signatures), and deployment triggers. MLflow Model Registry, SageMaker Model Registry, and Vertex AI Model Registry are the main implementations.
Reference details
| Topic | infrastructure |
|---|---|
| Last reviewed | 2026-06-24 |
Related terms
Example: The rollback test
The question a registry has to answer is: production is behaving badly, what exactly is deployed, and how do I get back? A useful entry pins the artifact hash, the training or fine-tuning run that produced it, the dataset snapshot, the evaluation run that cleared it, and who approved the promotion. Rollback then becomes repointing the production alias at the previous version — seconds, no rebuild. Without it, rollback means reconstructing a build from memory during an incident, which is when reconstruction is least reliable.
Commonly confused with
Three things get conflated. Experiment tracking records runs and metrics during development. A model registry governs which versioned artifact is promoted to which stage. A model hub distributes models to other people. MLflow ships the first two together, which is why the distinction blurs.
When to use it
Reach for it when:
- More than one model version can be in production at once, or you run staged rollouts
- Promotion needs an approval trail — regulated settings, or anything customer-facing
- Fine-tuned models, where the artifact is yours and nobody else can re-serve it for you
Reach for something else when:
- A single hosted third-party model behind an API — pin the model string in config instead
- Prototypes, where the overhead outweighs the traceability
- As the only lineage record: an entry pointing at a deleted dataset proves nothing
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Model Registry — AI Glossary.
Frequently asked questions
What is Model Registry?
A centralized store for managing the lifecycle of trained models from experimentation through staging to production deployment.
What concepts are related to Model Registry?
Closely related concepts include model hub, experiment tracking, mlflow.