What is AI Model Governance
Model control processes throughout the entire life cycle: from selection and training to launch, monitoring, updating and decommissioning.
Definition
Managing AI models helps you stay in control of which model is running, what data it was trained on, who approved it, what risks are known, and when it needs to be updated. This is especially important when the company has many models, versions and teams.
Example
After updating the support model, the team commits the version, compares metrics, checks security, and stores information about the reasons for the transition.
Why it matters
The term is important for production AI: a model is not a one-time file, but a system that needs to be maintained.
How it works
Management includes model registries, documentation, versions, accesses, metrics, monitoring, auditing, responsible roles and change procedures.
Where it is used
- MLOps
- corporate AI systems
- model version control
Limitations
Without discipline, it's easy to launch the wrong version, lose reproducibility, or not notice the degradation in quality after data changes.