ML Model Registry: Source of Truth for Production Models

WHY IT EXISTS: ML code is only half the deliverable. The other half is the trained artifact: weights, hyperparameters, feature engineering logic, and training data lineage. Without a system to track these artifacts, teams lose reproducibility. A data scientist cannot prove which model generated last week's predictions, and an engineer cannot roll back a bad deployment safely. The registry exists to close this gap between experimentation and production, treating models as versioned assets rather than disposable files.

THE MENTAL MODEL: Think of it as a library card catalog for your models. Each card holds metadata: who trained it, on what data, what metrics it achieved, and whether it passed review. The catalog does not store the books themselves; it points to them. In practice, the registry holds pointers to blob storage, but its real value is the structured metadata and state machine around each artifact.

HOW IT WORKS: A registry typically integrates with training pipelines. When a run finishes, the framework logs the serialized model, its signature, dependencies, and metrics to the registry. The artifact receives a versioned ID. Teams then promote versions through stages: staging, production, archived. Access controls ensure only approved roles can move a model to production. Some registries also capture the Docker image, feature store references, and model explanations. When a serving system loads a model, it queries the registry by stage or version rather than by file path.

WHEN TO USE IT: Use a registry once you have more than one model in production or when multiple people share training infrastructure. It is essential when compliance requires audit trails, such as in finance or healthcare. It also becomes critical when you run A/B tests between model versions or when you need to hot-swap a model without redeploying the entire service.

WHEN NOT TO USE IT: A registry adds operational overhead. For a single researcher running ad-hoc experiments on a laptop, a simple directory of dated files is faster. If your deployment process is still manual and your team has no automated retraining, the registry will be an empty catalog that nobody maintains.

ONE CANONICAL EXAMPLE: A retail company trains a demand-forecasting model weekly. The data scientist pushes version 4.2.1 to the registry after validation. The MLOps engineer promotes it to production after a shadow test. Two months later, metrics degrade. Because the registry links version 4.2.1 to its training dataset and code commit, the team rolls back to version 4.1.3 in minutes instead of retraining from scratch.