# MLflow (/docs/coreai/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 CoreAI 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 CoreAI 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]

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