- Course
- AI
Version Control for AI
AI version control is essential for managing models effectively. This course will teach you how to use MLflow for experiment tracking while offering an overview of other AI version control practices like Git, DVC, and CI/CD.
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.
Table of contents
About the author
A DevOps Engineer by profession, passionate about technologies, Uzair Ansari has expertise in Windows PowerShell, Windows Active Directory, public key infrastructure and Windows Servers. He likes to learn and share his knowledge with others.