Udemy – MLflow for MLOps & LLMOps: Master MLflow with Databricks

In this course, you will learn how MLflow is used in real-world MLOps systems to manage the entire machine learning lifecycle. Instead of focusing only on APIs, this course explains the system-level thinking behind MLflow so you can understand how ML systems are built in production environments.

What You Will Learn

By the end of this course, you will understand how to:

• Track machine learning experiments using MLflow
• Log parameters, metrics, artifacts, and runs
• Use MLflow Model Registry to manage model versions
• Deploy models using MLflow model serving
• Understand backend store and artifact store architecture
• Implement nested runs for advanced experiment tracking
• Use MLflow for LLMOps workflows including prompt registry
• Evaluate prompts and manage prompt versions
• Integrate MLflow with Databricks workflows
• Use Databricks AI Functions for AI-powered SQL tasks