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 A model registry that retains superseded versions can leave an old, uncorrected model's retention exposure live even after a corrected version has replaced it in production.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 Certified AI Data Governance Professional (CADGP).
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.
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 A model registry that retains superseded versions can leave an old, uncorrected model's retention exposure live even after a corrected version has replaced it in production.
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.
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 module of every program is free with a free account.
- Change Management · Rollout, Monitoring, and Incident Response, Certified Agentic AI Governance Professional (CAAGP)
- Retention versus the model that memorized: deleting data a model already learned from · Consent, Purpose, and the Law of Data, Certified AI Data Governance Professional (CADGP)
- Model cards and system cards for your own systems, written so an outsider could act on them · Evidence Engineering, Certified AI Governance Professional (CAIGP)
Where it shows up at work
A definition is where Model registry starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Spots a regulatory change, decides applicability, assigns actions, updates controls and keeps the implementation evidence.
One lesson on this page teach it as a primary outcome.
Sets performance metrics, thresholds and review triggers after launch, and treats a model change, a vendor update or new data as a reason to re-check.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- Chief Compliance Officer, AI compliance editionneeds it at depth
- Senior AI Compliance Analystneeds it at depth
- AI Compliance Managerneeds it at depth · 2 open postings this week
- AI Compliance Specialistneeds it at depth · 1 open posting this week
- Compliance Analystneeds working knowledge · 2 open postings this week
- VP of AI Governance and Ethicsneeds working knowledge
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: ISO/IEC 42001 Clause 4.2; ISO 37301; EU AI Act Article 72; NIST AI RMF MEASURE 3 and MANAGE 4.
One interview question about it
A new regulation lands. How do you decide what changes in your program by when, and how do you prove you noticed in time?
Asked of candidates for regulatory change management. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Compliance and regulatory affairs carries it as work you can show: Regulatory change logs and applicability decisions are your work product.
- Legal and paralegal work carries it as a claim to back up: Tracking regulatory change is habitual.
- Model risk, credit risk and quantitative analysis carries it as a claim to back up: Performance monitoring thresholds are familiar.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.