A model registry is a versioned store for trained ML models and their metadata. It answers questions that every production ML team eventually faces: which model is currently deployed, what data was it trained on, who approved it, and how does it compare to the previous version. Without a registry, this information lives in spreadsheets, Slack messages, and individual memory.

What a Model Registry Stores

Model artifacts - The serialized model files (weights, architecture, preprocessing pipelines) that can be loaded for inference. Each version is immutable and addressable.

Metadata - Training parameters, evaluation metrics, dataset references, training duration, framework version, and any custom properties relevant to your domain.

Lineage - Links to the experiment run that produced the model, the training data version, the feature definitions used, and the code commit. This chain enables full reproducibility.

Stage labels - A model version moves through stages: development, staging, production, archived. Stage transitions can be gated by automated checks and human approval.

Designing Your Registry Workflow

Registration. When an experiment produces a model worth preserving, it is registered with its artifacts and metadata. Automated pipelines should register models that pass evaluation thresholds. Data scientists can also register models manually from exploratory work.

Evaluation. Before a model advances to staging, run it through your evaluation suite: aggregate metrics, slice-based analysis, fairness checks, latency benchmarks, and comparison against the current production model. Store all evaluation results as metadata on the model version.

Approval. Define who can promote a model to production. For low-risk applications, automated promotion based on evaluation results may suffice. For high-risk applications, require human review and sign-off. The registry records who approved each transition and when.

Deployment. The deployment pipeline pulls the production-stage model from the registry. This decouples model training from model deployment. You can retrain frequently and deploy selectively.

Retirement. When a model version is replaced, archive it rather than deleting it. You may need to roll back, audit past decisions, or compare performance trends across versions.

Implementation Options

MLflow Model Registry is the most widely adopted open-source option. It integrates with MLflow tracking, supports stage transitions, and provides a UI and API for model management. It works well for teams already using MLflow for experiment tracking.

SageMaker Model Registry integrates tightly with the AWS ML ecosystem. It supports approval workflows, model groups, and direct deployment to SageMaker endpoints.

Weights & Biases Model Registry connects model versions to experiment runs and provides collaborative review features. Strong choice for teams using W&B for experiment tracking.

Custom registries built on object storage and a metadata database are an option for teams with unique requirements, but the operational overhead is significant and rarely justified.

Best Practices

Automate registration from pipelines. Manual registration leads to inconsistency and missing metadata. Your training pipeline should register models automatically with all relevant metadata.

Version everything together. A model is not just weights. Version the preprocessing code, feature definitions, and inference code alongside the model artifacts. When you load a model version, you should get everything needed to run it.

Enforce metadata requirements. Require minimum metadata before a model can be registered: dataset version, evaluation metrics, training parameters. Missing metadata makes a registry useless for governance and debugging.

Tag models meaningfully. Beyond stage labels, use tags for the use case, team, regulatory classification, and deployment target. Tags make the registry searchable as it grows.

Monitor after deployment. The registry should link to production monitoring dashboards for each deployed model. When drift is detected, the alert should reference the specific model version and its lineage.