MLflow

Component Category

Model registry, ML lifecycle, and GenAI observability

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

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

• MLflow Documentation
• MLflow on GitHub

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