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Model registry

A system for tracking and managing versions of trained models, including which version is currently deployed, which are archived, and their associated metadata. A model registry that retains superseded versions can leave an old, uncorrected model's retention exposure live even after a corrected version has replaced it in production.

Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in AI Data Governance: The Data Chair.

How each discipline defines it

The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.

AI Data Governance: The Data Chair

A system for tracking and managing versions of trained models, including which version is currently deployed, which are archived, and their associated metadata. A model registry that retains superseded versions can leave an old, uncorrected model's retention exposure live even after a corrected version has replaced it in production.

AI Governance: Applied Mastery

An internal catalogue of an organization's models and their versions. When a maintained card is a required field in the registry rather than an optional attachment, documentation coverage rises sharply and staleness becomes visible, which is the documentation-by-default lesson from public model hubs applied internally.

Agentic AI Governance: Applied Mastery

A single, authoritative record of which model version is approved for a given deployment, used to make model-update technical triggers detectable by tooling rather than dependent on memory.

Where it is taught

The exact lessons this term appears in. The first 7 topics of every program are free with a free account.

Terms it appears with

Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.