Model Registry
A centralized repository for storing, versioning, and managing machine learning models throughout their lifecycle from development to production.
In Depth
A model registry is a centralized catalog and storage system that tracks machine learning models throughout their lifecycle, from initial training through deployment and eventual retirement. It serves as the single source of truth for model metadata, versions, lineage, and deployment status, enabling teams to manage the growing portfolio of models that power enterprise AI applications.
Core model registry capabilities include model versioning with immutable artifacts, ensuring any model version can be reproduced and audited; metadata management storing training parameters, evaluation metrics, data lineage, and deployment history; stage management tracking models through development, staging, and production phases; access control governing who can register, approve, and deploy models; and integration with CI/CD systems for automated promotion and deployment workflows.
Popular model registry implementations include MLflow Model Registry (open-source, widely adopted), Weights and Biases Model Registry (integrated with experiment tracking), cloud-native registries from AWS SageMaker, Azure ML, and Google Vertex AI, and container registries adapted for model serving. The choice depends on existing infrastructure, team workflow preferences, and integration requirements with serving and monitoring systems.
For regulated industries, model registries provide essential governance capabilities: complete audit trails of model changes, approvals, and deployments; reproducibility guarantees that any past prediction can be explained using the exact model version and data that produced it; compliance documentation linking models to their validation reports and risk assessments; and retirement workflows that ensure deprecated models are properly decommissioned. As organizations scale from a few models to hundreds, the model registry becomes critical infrastructure for maintaining operational control and organizational knowledge.
Related Terms
MLOps
A set of practices combining machine learning, DevOps, and data engineering to reliably deploy and maintain ML models in production.
Model Serving
The infrastructure and systems that host trained AI models and handle incoming prediction requests in production environments.
Model Monitoring
The practice of continuously tracking AI model performance, data quality, and system health in production to detect degradation and trigger remediation.
Machine Learning
A branch of artificial intelligence where systems learn patterns from data to make predictions or decisions without being explicitly programmed for each scenario.
Feature Store
A centralized platform for managing, storing, and serving machine learning features consistently across training and inference pipelines.
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