In this project , we are demonstrating how MLFLow can be used in machine learning. MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It simplifies experiment tracking, reproducibility, model management, and deployment.
MLflow provides four main components:
- Tracking: Logging and organizing experiments.
- Projects: Packaging code into reproducible runs.
- Models: Managing machine learning models.
- Registry: Managing and versioning models in a central repository.
pip install mlflow
- Chapter_1_Basics
- Create , retrieve and delete experiment by lib functions and customized functions
- Understanding the ml flow runs.
- Model logging
- Nested Model logging.