MLflow for MLOps & LLMOps: Master MLflow with Databricks

About this course

Machine learning projects often start as simple notebooks, but as teams grow and models move toward production, managing experiments, models, and deployments becomes difficult.

How do teams track experiments?

How do they manage model versions?

How do they deploy models reliably?

And how do modern teams manage LLM prompts and GenAI workflows?

This is where MLflow comes in.

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

Practical Learning Approach

This course focuses on hands-on demonstrations.

You will learn how to:

• Set up MLflow from scratch
• Track experiments locally
• Understand MLflow’s internal architecture
• Log and manage machine learning models
• Deploy models as REST APIs
• Build prompt management workflows for LLM applications
• Use MLflow together with Databricks

Who This Course Is For

This course is ideal for:

Machine Lea

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