Model Registry Explained¶

Introduction¶

A model registry is the inventory of model artifacts that are candidates for deployment or already deployed. It should answer which model exists, who approved it, what metrics it had, and where it is running.

Why This Matters¶

Without a registry, teams pass model files around in buckets or folders. That breaks auditability and makes rollback risky.

Core Concepts¶

A registry tracks immutable model versions, lifecycle stage, Git commit, data version, metrics, approval, artifact URI, and deployment link.

Practical Example¶

A registry record can be represented as metadata even before adopting a full platform:

model: churn-risk
version: 17
stage: staging
artifact: s3://ml-artifacts/churn/17/model.pkl
git_commit: 9f3a21c
data_version: customers-2026-05-30
metrics:
  f1: 0.842
  precision: 0.81
  recall: 0.875
approved_by: platform-ml-review

How This Fits in a Production Workflow¶

Deployment should pull a model by approved version, not by an untracked local filename. Rollback should mean selecting the previous registry version and redeploying.

Common Mistakes¶

• Overwriting model artifacts.
• Calling a model production without approval metadata.
• Storing metrics separately from the model version.
• Not recording which deployed service uses which model version.

Quick Checklist¶

• Is every model artifact immutable?
• Does each version include metrics and data version?
• Is production approval recorded?
• Is rollback documented?
• Can the running API report its model version?

Related Guides¶

• Experiment Tracking in MLOps Explained
• Model Deployment Strategies in MLOps
• MLOps Production Checklist

Summary¶

Learn how a model registry tracks model versions, stages, approvals, rollback options, and production metadata.