# MLflow (/docs/ai/components/mlflow)



## Component Category [#component-category]

Model registry, ML lifecycle, and GenAI observability

## Component Description [#component-description]

MLflow is the model lifecycle service used in AI for experiment tracking, model registration, controlled model version management, and GenAI observability.

## Why It Is Used [#why-it-is-used]

It gives teams a structured way to manage model artifacts and lifecycle steps before models are exposed to applications and inference services. It is also the default destination for GenAI traces emitted by the AI LLM backend through OpenTelemetry.

## Learn More [#learn-more]

* [MLflow Documentation](https://mlflow.org/docs/latest/)
* [MLflow on GitHub](https://github.com/mlflow/mlflow)

## Interacts With [#interacts-with]

* `AI Web Portal` for user-facing lifecycle interactions
* `BSQAI API` for backend model-aware workflows and AI observability traces
* `Model Installer` for deploying tracked models into Foundation inference
* `OpenTelemetry` for GenAI trace export
* `Rook Ceph` for artifact storage through RGW
* `PostgreSQL` for persistent service state
* `Keycloak` for authentication and protected access