How to Build a Model Registry (Playbook)

When a regulator, an auditor, or an incident review asks which model made a decision, how it was validated, and who approved it, the answer should take minutes, not an archaeology project. A model registry is the system of record that makes that true: immutable versions, lineage, evaluation records, approval workflow, and deployment tracking. This playbook covers the build, following FISTA's AI enablement practice. Governance context is in ai model governance and ai model risk management.

What does the registry do?

Component	Function
Model record	Identity, purpose, owner, risk tier, intended use
Versions	Immutable artifacts or references with metadata
Lineage	Links to code, data, feature, and prompt versions
Evaluation records	Metrics, reports, fairness and safety results per version
Documentation	Model or system cards, limitations, change notes
Approval workflow	Stages, reviewers, evidence, decisions
Deployment tracking	Which version runs where, since when
Access control	Roles for registration, approval, deployment, viewing

Step 1: Define the model record and version schema

Specify what a model record contains and what each version must carry before it can advance: artifact reference, training code commit, data snapshot identifiers, feature set versions, parameters, and for LLM applications the base model version, prompt versions, retrieval configuration, and tool set. Versions are immutable; corrections create new versions. Documentation expectations follow what is a model card.

Step 2: Capture lineage automatically

Integrate with training pipelines and the prompt management system so lineage is recorded at creation rather than typed later. Link to the feature store for feature versions and to the data platform for dataset snapshots. Lineage supports reconstruction and impact analysis when a data or feature issue is discovered. See what is data lineage in ai and how to build a feature store.

Step 3: Attach evaluation records

Every version carries its evaluation results: metrics by category, fairness and safety test outcomes, comparison against the current production version, and links to full reports. The evaluation service writes these automatically. Versions without evaluation records cannot advance. Method is in the AI evaluation and testing whitepaper.

Step 4: Implement the approval workflow

Define stages: candidate, validated, approved, deployed, retired. Each transition has required evidence and authorized roles; for higher-risk models, validation is performed by a function independent of the builders. Record reviewer identity, decision, and rationale. The workflow is the operational form of model risk management. See the agentic AI governance whitepaper.

Step 5: Track deployment

Deployment systems fetch only approved versions and report deployments back to the registry with environment, endpoint, and timestamps. Registry state must reflect reality; side-channel deployments are blocked by making the registry the only source of deployable artifacts. See what is model deployment and how to build a ci-cd pipeline for machine learning.

Step 6: Control access

Roles for registering versions, running validation, approving, deploying, and viewing; separation between builders and approvers for higher-risk tiers; audit logging of every registry action. Access design follows ai access control.

Step 7: Integrate with monitoring

Link production monitoring to registry versions so drift, quality, and incident data are attributed to the version that produced them, and so retirement and rollback decisions reference evidence. See the AI observability whitepaper.

Step 8: Report

Produce the inventory view governance needs: models by risk tier, versions in production with approval dates, pending validations, models past review dates, and models affected by a given data or feature issue. This is the register that regulators and internal audit ask for. See ai audit and accountability.

Worked example: a lender's model estate

A lender registers its credit, fraud, collections, and document-extraction models and its LLM-based servicing assistant configurations in one registry. Training pipelines write versions with lineage automatically; the evaluation service attaches metrics, fairness results, and comparisons. Higher-risk credit models require independent validation before approval, recorded with reviewer identity. Deployment pulls approved versions only and reports endpoints back. When a data vendor reports an error in a historical feed, lineage identifies the three model versions trained on it and the decisions they influenced. When the servicing assistant's provider updates its base model, the new configuration is registered as a candidate, evaluated, approved, and deployed with a record, and the audit report for the quarter is generated from the registry rather than assembled by hand.

What does it cost?

The registry is modest infrastructure, and open-source and managed options exist; the cost is mostly integration and discipline. It repays itself the first time an incident or audit is resolved from records. See mlops platform cost.

What are the common mistakes?

• A registry that is a spreadsheet updated after the fact.
• Versions without lineage, so reconstruction is impossible.
• Approval as a checkbox with no evidence attached.
• Deployments that bypass the registry.
• LLM applications left out because they are "not models."
• No link between monitoring data and versions.

What are the prerequisites?

A registry is only as good as the pipelines that write to it. Before building, confirm that training runs can be reproduced from recorded inputs, that evaluation produces machine-readable results, and that deployment can be constrained to pull from a single source. Where any of these is missing, the registry will fill with unverifiable entries. Sequence the work so reproducibility and evaluation automation land alongside the registry, and enforce the deployment constraint from the first registered model rather than retrofitting it.

Who owns the registry?

The platform or MLOps team owns the registry as infrastructure; model owners own their records; the model risk or governance function owns approval stages for higher tiers.

How do you handle emergency rollbacks?

Rollback should be a pointer change to the previous approved version, rehearsed before it is needed, with the registry recording who rolled back, why, and which evaluation evidence supported the prior version. Emergency changes get the same record as planned ones; the difference is that review happens within a day afterward rather than beforehand.

How FISTA Solutions builds model registries

FISTA Solutions builds model registries to this playbook as part of its AI enablement platform: immutable versions with automatic lineage, attached evaluation records, staged approval with role separation, deployment tracking as the sole source of deployable artifacts, access control, monitoring integration, and governance reporting, covering both classical models and LLM application configurations. Every system delivered through AI agents is registered this way, and forward deployed engineers integrate the registry with your model risk and audit processes. The record behind the work is 150+ projects with 99.9% uptime.

To scope a model registry, message FISTA on WhatsApp, or read mlops services for the broader operating capability.