Model Registration

Executive Definition: Model Registration — The process of cataloging and versioning AI models within an MLOps pipeline to track metadata, lineage, and deployment status.

A model registry acts as a centralized repository for trained models, recording versions, hyperparameters, evaluation metrics, and training data lineage. This provides an auditable history of deployed models and their performance, which is critical for debugging production issues and ensuring regulatory compliance.

Frequently Asked Questions

What metadata should a model registry track?

Registries typically track model versions, training dataset lineage, hyperparameters, evaluation metrics, timestamps, and deployment environment statuses.

What are some common model registry tools?

Widely used tools include the MLflow Model Registry, Weights & Biases, Neptune.ai, AWS SageMaker Model Registry, and Azure Machine Learning Model Registry.

Is a model registry necessary for a single model?

Yes, even for a single model, a registry is highly recommended. It tracks version history and metrics over time, and provides an essential audit trail for rollback and compliance.