MLflow Tutorial for Machine Learning in Python
Track every machine learning experiment you run in Python with MLflow: parameters, metrics, charts and models, compared side by side in the MLflow UI. Beginner-friendly parts take you from installing MLflow and running your own tracking server to evaluating, registering and serving scikit-learn models, all on your own computer.
Start with Part 1Install MLflow and Track Your First Experiment
Install MLflow 3.18 with uv, start your own tracking server on your computer, and log your first machine learning experiment from Python: parameters, metrics, tags and a chart for a scikit-learn house price model, then compare the runs in the MLflow UI.
