What is Model Registration?
Model Registration — The process of cataloging and versioning AI models within an MLOps pipeline for tracking and deployment.
Model registration catalogs every trained model with its version, metrics, training data lineage, and deployment status. It provides an auditable history of what was deployed, when, and how it performed — critical for regulated industries and debugging production issues.
Frequently Asked Questions
What information should a model registry track?
Model version, training dataset version, hyperparameters, evaluation metrics, training date, deployment status, and any relevant compliance documentation.
What tools provide model registration?
MLflow Model Registry, Weights & Biases, Neptune.ai, AWS SageMaker Model Registry, and Azure ML Model Registry are the most popular options.
Do I need a model registry for one model?
Even with one model, a registry tracks versions and metrics over time. It becomes essential when managing multiple models or when regulatory compliance requires audit trails.