Is a model inventory the same as a model registry?

Not necessarily. A registry often focuses on technical artifacts and versions, while an inventory can include organizational purpose, ownership and oversight relationships.

Should retired models disappear?

Usually their status should change while relevant historical records remain available under the organization's retention approach.

Can one model have multiple inventory-linked deployments?

Yes. Preserving those links is essential for accurate impact analysis and asset counts.

AI Glossary / Statistics and probability / Intermediate

Model inventory

Definition

A model inventory is an organized, maintained record of the models an organization develops, obtains or uses, together with information needed to understand their ownership, purpose, versions, deployment and oversight. It may be part of a broader AI system inventory that includes surrounding applications, data and operational dependencies. The inventory provides a navigable view of assets and their status rather than merely a folder of model files. Its scope and unit of record should be defined explicitly so that models, versions and deployments are not confused.

What it means

Organizations can lose track of where models are used, who is responsible for them and which decisions depend on them. An inventory makes those relationships visible. A single model may have several versions and many deployments, while one application may use multiple models. External services and retired systems may also matter for impact assessment and historical traceability. The inventory does not itself prove that a model is valid or safe; it helps locate the evidence, owners and dependencies needed to assess and manage it.

How it works

Define inclusion criteria and identifiers for models, versions, systems and deployments. Record purpose, owner, lifecycle status, intended users, data dependencies, evaluation evidence and relevant operational links. Connect entries to registries, source records and deployment systems where feasible, while keeping business and oversight information understandable. Establish update triggers for development, release, changes, incidents and retirement. Reconcile the inventory against actual usage and procurement records. Assign responsibility for correcting stale or missing entries, and control access to sensitive details without making essential ownership and status information inaccessible.

Three practical examples

Incident response: find every deployment using a model version affected by a newly discovered defect.
Portfolio management: identify duplicate models, unclear ownership or applications awaiting review.
Change planning: trace which services and data pipelines could be affected when a model is replaced or retired. The value depends on accurate relationships and timely updates, not simply on the number of fields collected.

Think of it this way

Think of a maintained equipment register linked to operating locations, service histories and responsible staff. Knowing that an organization owns three machines is different from knowing which versions are installed at four sites. The register helps people find and manage assets, but an entry marked reviewed is useful only when it links to actual review evidence and remains current.

Worked example

Consider an illustrative organization with three model identities: M1, M2 and M3. M1 has versions v1 and v2, M2 has v1, and M3 has v1 and v2, for five stored versions. Four active deployments use M1v2 twice, M2v1 once and M3v1 once. The correct counts are three model identities, five versions and four deployments, with three distinct versions currently deployed. If a defect affects M3v1, the deployment links identify one affected live deployment even though M3v2 also exists in storage. Counting inventory rows without defining their type could produce misleading totals. If a reconciliation later discovers a sixth version that was omitted, the previously recorded five represent 5/6, approximately 83.3%, of the now-known version set. That fraction measures this reconciliation's completeness, not proof that no further unknown assets exist.

Illustrative case study

In a fictional case, Sequoia Services creates an inventory after teams struggle to identify owners during a prediction outage. It connects model records to deployment locations and evidence rather than storing only model names. A reconciliation finds a departmental application using an external prediction service that was absent from the central registry. The organization adds the application and its accountable owner, then introduces an update step in its release process. The inventory becomes a maintained operational resource rather than a one-time spreadsheet exercise.

Common misconceptions and mistakes

An inventory is not automatically complete because every registered model appears in it; unregistered or externally supplied systems may still be missing. A model registry and an inventory overlap but often serve different purposes: artifact versioning alone may omit business ownership and use context. Counts are meaningless without a defined unit. Retired entries can remain relevant for traceability. A status label is not a substitute for linked evidence, and excessive undocumented fields can create a large but unreliable database.

Business use

Businesses should design the inventory around recurring questions: what is deployed, who owns it, where is it used, what changed and what evidence supports that use? Keep the minimum information sufficient for those questions and link to detailed records maintained elsewhere. Measure freshness and reconcile against independent operational sources. Include supplier dependencies and replacement or retirement plans where relevant. Use the inventory to support decisions and incident response, while separately evaluating the quality of the models and controls it describes.

Check your understanding

Why are there four deployments but only three deployed versions in the example?

Answer: M1v2 is used in two separate deployments. A deployment count measures operating instances or uses under the defined scheme, while the distinct-version count removes that duplication. Both are useful, but they answer different questions.

Related terms

Model Registry
Deployment
AI system inventory
model version
lineage
ownership
lifecycle status
model documentation
dependency mapping

Learn next

Study how model identities, versions and deployments relate, then design reconciliation checks against release and procurement records. Learn how inventories connect to documentation, incident response and retirement without duplicating every technical artifact.

Incident response

Sources

NIST AI RMF Playbook, GOVERN 1.6, AI system inventories: https://airc.nist.gov/airmf-resources/playbook/govern/ . The guidance describes inventory purposes and possible records. The model-version-deployment example and fictional services case above are original illustrations, not a mandatory universal inventory schema.

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