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.