Model Registry
A centralized repository for storing, versioning, and managing trained ML models along with their metadata (metrics, parameters, lineage). It serves as the system of record for models.
Why It Matters
A model registry prevents the chaos of model files scattered across team members' laptops. It enables reproducibility, governance, and orderly deployment.
Example
MLflow Model Registry storing version 3.2 of the fraud model with its accuracy metrics, training data hash, hyperparameters, and approval status — ready for production deployment.
Think of it like...
Like a library catalog system for models — every model is registered, versioned, and accompanied by its metadata so anyone can find, evaluate, and deploy it.
Related Terms
MLOps
Machine Learning Operations — the set of practices that combine ML, DevOps, and data engineering to deploy and maintain ML models in production reliably and efficiently.
Model Serving
The infrastructure and process of deploying trained ML models to production where they can receive requests and return predictions in real time. It includes scaling, load balancing, and version management.
Deployment
The process of making a trained ML model available for use in production applications. Deployment involves packaging the model, setting up serving infrastructure, and establishing monitoring.
Model Monitoring
The practice of continuously tracking an ML model's performance, predictions, and input data in production to detect degradation, drift, or anomalies after deployment.