Model Registry
A model registry is a centralized system for storing, versioning, and tracking machine learning models throughout their lifecycle, recording metadata such as training data, performance metrics, and approval status for each version.
Definition
A model registry is a centralized system for storing, versioning, and tracking machine learning models throughout their lifecycle, recording metadata such as training data, performance metrics, and approval status for each version.
Overview
Once an organization has more than a handful of models in production, keeping track of which version of which model is deployed where — and whether it was properly tested and approved — becomes difficult to manage informally. A model registry addresses this by giving every trained model a versioned, auditable record: what data and code produced it, how it performed on evaluation metrics, who approved it, and where it's currently deployed. A typical model registry workflow starts when a data scientist trains a candidate model and registers it, attaching metadata like the confusion matrix, F1 score, or other evaluation results. The registry then tracks the model through stages — such as staging, production, and archived — often with approval gates that require sign-off before a model can be promoted to serve live traffic. This creates a clear audit trail, which is valuable both for debugging ('which model version produced this prediction?') and for AI governance and regulatory compliance. Model registries are typically one part of a broader MLOps platform, working alongside experiment tracking tools that log training runs and hyperparameters, a feature store that manages model inputs, and monitoring systems that watch for model drift once a model is live. Popular implementations include open-source and cloud-native tools that integrate with training pipelines; these concepts are covered hands-on in courses like MLOps & Model Deployment.
Key Concepts
- Provides versioned, auditable tracking of trained models across their lifecycle
- Records metadata such as training data lineage, evaluation metrics, and approvals
- Manages model promotion through stages like staging, production, and archived
- Creates an audit trail linking live predictions back to a specific model version
- Supports rollback to a previous model version if a new deployment underperforms
- Integrates with experiment tracking, feature stores, and drift-monitoring tools
- Supports AI governance and regulatory compliance requirements around model approval