Summary  
The chapter introduces a framework for tracking experiment parameters, metrics, and artifacts, packaging code for reproducibility, standardizing artifact storage, and managing artifact versions.

General domain of usage  
Machine learning lifecycle management

MLflow is one of the most popular **open-source tools for managing the machine learning lifecycle**.
It helps track experiments, manage models, and streamline workflows from training to deployment.
MLflow provides a unified interface for **experiment tracking**, **model packaging**, and **model registry**, making it an essential tool in modern MLOps.

## **Key Components of MLflow**

1. **MLflow Tracking** — records parameters, metrics, and artifacts (like models or plots) for each run;
2. **MLflow Projects** — allows you to package code in a reproducible format;
3. **MLflow Models** — standardizes model storage and deployment across different frameworks;
4. **MLflow Registry** — serves as a central repository to version and manage models.

**MLflow** — an open-source platform for managing the end-to-end machine learning lifecycle, including tracking, packaging, and deploying models.

Definition

You can use MLflow locally or with cloud-based backends.
It integrates easily with frameworks like **scikit-learn**, **TensorFlow**, **PyTorch**, and **XGBoost** — all without modifying existing training code.


Note

Which of the following is *not* a core MLflow component?

Master practical MLOps workflows for deploying, tracking, and maintaining machine learning models in production. Learn to use MLflow, FastAPI, Docker, Airflow, and scikit-learn to automate and scale your ML projects.

Explore the foundations of MLOps, its lifecycle, and the essential tools that enable scalable, reliable machine learning workflows.

Learn to track, compare, and manage machine learning experiments and models using MLflow.

Deploy machine learning models as scalable APIs using FastAPI and Docker for real-world serving.

Automate and schedule end-to-end ML workflows using scikit-learn pipelines and Apache Airflow.

Monitor deployed models for drift, and implement CI/CD for robust, automated ML systems.

Introduction to MLflow

Key Components of MLflow