# What is Model Registry? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Model Operations  
> **Canonical Citation:** [https://spidits.com/ai-glossary/model-registry](https://spidits.com/ai-glossary/model-registry)

## Definition
A Model Registry is a centralized repository store for managing the lifecycle of machine learning models. It stores model weights, parameter logs, version details, and deployment states.

## Architectural Mechanics & Deep Dive
A model registry is a centralized catalog and repository used to manage the lifecycle of machine learning models. It tracks model versions, training metadata, evaluation metrics, and deployment states (e.g., staging, production), serving as a crucial component of MLOps pipelines to ensure accountability and auditability of models.

## Industry Applications & Real-World Use Cases
MLOps infrastructure, deployment tracking, and automated model testing pipelines.

## Frequently Asked Questions

### Why is a model registry important in MLOps?
It enables developers to track model versions, audit training runs, and automate migrations from staging to production.

### What are popular model registry tools?
MLflow Model Registry, Weights & Biases Artifacts, and Hugging Face Hub.

## Related Terminology
- [observability](https://spidits.com/ai-glossary/observability)
- [inference](https://spidits.com/ai-glossary/inference)
- [models](https://spidits.com/ai-glossary/models)

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*From the SPIDITS AI Knowledge Base (300+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*