This project demonstrates how to track, manage, and visualize machine learning experiments using MLflow and integrate them with DagsHub for version control and collaboration. It covers experiment tracking, model logging, and reproducible experiments in a collaborative environment.
- Experiment tracking with MLflow
- Model logging and versioning
- Integration with DagsHub for collaboration
- Visualization of experiment metrics
- Reproducible ML workflows
- Python 3.10+
- Libraries:
MLflow– Experiment trackingDagsHub– Version control and collaborationpandas/numpy– Data manipulationscikit-learn– ML modelsmatplotlib/seaborn– Visualization- Jupyter Notebook