What you'll learn

Managing AI models without proper version control can lead to inconsistencies, lost experiments, and deployment challenges. In this course, Version Control for AI, you’ll learn to implement MLflow for efficiently tracking experiments. First, you’ll explore the importance of AI version control and get an overview of Git, DVC, and CI/CD. Next, you’ll discover how to use MLflow for tracking machine learning experiments. Finally, you’ll learn how to explore details logged in the experiments and evaluate and compare models. When you’re finished with this course, you’ll have the skills and knowledge of MLflow needed to ensure reliable and reproducible AI model development.