What is AI Model Registry
A system for recording models, their versions, statuses, metrics, owners and change history.
Definition
A registry of AI models is needed so that the team knows what models exist, where they are used, who is responsible for them, what version is in production, and what tests have been passed. This is especially important in companies where models are updated regularly and affect real processes.
Example
The team sees that the 2.3 case classification model is working in support, has been audited, and has a responsible owner.
Why it matters
The term is important for the reliable implementation of AI: without a registry, the model infrastructure quickly turns into chaos.
How it works
The registry stores model cards, versions, artifacts, metrics, lifecycle stages, data links, approval logs, and sometimes audit results.
Where it is used
- version control
- MLOps
- corporate model control
Limitations
The registry is only useful for update discipline. If teams don't enter data or bypass the process, the system quickly becomes outdated.