This MLOps course focuses on building end-to-end machine learning systems using MLflow and Databricks, two of the most widely used tools in modern machine learning engineering. It is designed for data scientists and ML engineers who want to move from model development to production-ready AI systems.

The course begins with an introduction to MLOps principles and explains how machine learning lifecycle management works in real-world environments. You will learn how Databricks provides a scalable platform for data processing, model training, and collaboration.

A major focus of the course is MLflow, which is used for experiment tracking, model logging, and version management. You will learn how to track experiments, compare model performance, and organize different model versions for reproducibility and deployment.

The course also covers model registration, where trained models are stored and managed in a centralized registry for easy deployment. You will explore how to prepare models for production and ensure they meet quality and performance standards.

Additionally, you will learn how to deploy machine learning models into production environments using Databricks model serving capabilities. This enables real-time inference and scalable AI applications.

By the end of this course, you will be able to design and implement full MLOps pipelines using MLflow and Databricks, building reliable and production-grade machine learning systems.