MLflow
Open platform for ML lifecycle: experiment tracking, model registry and deployment.
Official website / GitHub ↗ Visit
What is this technology for?
Its role in production environments and why it is taught in our curriculums.
MLflow is the open platform for the ML lifecycle — experiment tracking, model registry and deployment make ML reproducible and production-ready.
What you will learn
The hands-on skills you will gain on this technology in our curriculums.
- Track experiments and metrics
- Register and version models
- Deploy models from the registry
- Manage the full ML lifecycle with governance
Latest News & Ecosystem Updates
Recent innovations, major releases, and key industry milestones in the ecosystem.
MLflow 3.0: Universal AI Model Registry & Automated LLM Evaluation
Comprehensive experiment tracking, hyperparameter tuning, model versioning, and automated evaluation metrics.
Seamless Model Staging & Production Deployment to OpenShift AI
Single-command deployment of certified models with automated production-ready REST/gRPC endpoints.
Featured in the curriculum
Find this technology in the following modules of our Red Hat certified curriculums.
AS300 — AI Platform Engineer
View curriculum →
Projet final (setup + sprints + dry-run)
Projet · Module 11 — AS300
