Model Registry
Definition
A model registry is a centralized repository for storing, versioning, and managing ML models throughout their lifecycle, tracking metadata like training parameters, performance metrics, and deployment status.
Why It Matters
Without a model registry, teams lose track of models. Which version is in production? What parameters trained it? Who approved it? When did performance start degrading? These questions become archaeological expeditions through notebooks, chat logs, and tribal knowledge.
A model registry brings software engineering discipline to ML. Every model version is tracked, immutable, and linked to its training context. Rollbacks are trivial since you just deploy a previous version. Audits are straightforward with full lineage from data to deployment.
For AI engineers, model registries matter even when using pre-trained LLMs. You might not train models from scratch, but you fine-tune them, create LoRA adapters, or develop prompt templates. All of these need versioning and lifecycle management.
Implementation Basics
A model registry tracks several types of information:
1. Model Artifacts The actual model files (weights, configs, tokenizers). Stored in object storage with references in the registry. Each version is immutable, so once registered, it never changes.
2. Metadata Training parameters (hyperparameters, data version, compute used). Evaluation metrics (accuracy, latency, cost). Dependencies (Python version, library versions). Custom tags and descriptions.
3. Lifecycle Stages Typical stages: Development → Staging → Production → Archived. Transitions can require approvals and trigger automated validation. Clear ownership and audit trails for compliance.
4. Lineage Links to training data, experiment runs, and pipeline versions. Enables reproducibility and debugging. Critical for understanding why a model behaves as it does.
Implementation options: MLflow Model Registry (open-source, most popular), Weights & Biases, AWS SageMaker Model Registry, Vertex AI Model Registry, DVC.
Start simple: even a well-organized S3 bucket with naming conventions beats scattered model files. Graduate to a full registry when you have multiple models, team collaboration, or compliance requirements.
Source
The MLflow Model Registry provides a central hub for managing the full lifecycle of MLflow Models, including model versioning, stage transitions, and annotations.
https://mlflow.org/docs/latest/model-registry.html