Model Registry

A model registry is a centralised system for managing the lifecycle of AI models within an organisation. It tracks which models exist, which versions are deployed, how they perform, and who is responsible for them. Think of it as version control for AI models.

Why organisations need model registries

As companies adopt AI, they quickly accumulate models — a chatbot model, a classification model, several fine-tuned variants, experimental models being tested. Without a registry, teams lose track of what is deployed where, which version is running in production, and whether a model has been evaluated for bias or compliance.

What a model registry tracks

• Model metadata: Name, version, creator, creation date, framework, and architecture.
• Lineage: Which training data was used, what hyperparameters were set, and which parent model it was derived from.
• Performance metrics: Accuracy, latency, throughput, and any custom evaluation metrics.
• Deployment status: Where the model is running (staging, production, retired) and which applications depend on it.
• Approval workflows: Whether the model has been reviewed and approved for production use.
• Artefacts: The model files themselves, configuration files, and associated code.

Model registry vs model hub

A model hub is a public platform for sharing models across the community. A model registry is an internal tool for managing your organisation's models. Some platforms serve both purposes — Hugging Face, for example, offers private model registries alongside its public hub.

Popular model registry tools

• MLflow: Open-source platform with a built-in model registry.
• Weights & Biases: Experiment tracking with registry capabilities.
• Amazon SageMaker Model Registry: AWS's managed solution.
• Azure ML Model Registry: Microsoft's equivalent.
• Vertex AI Model Registry: Google Cloud's offering.

When you need one

A model registry becomes essential when your organisation has more than a handful of models in production, when multiple teams are building AI features, or when compliance requires auditability of AI systems. For small teams experimenting with a single model, a registry is premature optimisation.