This MLOps tutorial series introduces practical machine learning engineering workflows focused on building reproducible, scalable, and production-ready ML systems. It emphasizes modern DevOps practices applied specifically to machine learning, often referred to as MLOps.
The course starts with the fundamentals of Continuous Integration (CI) for machine learning, explaining how automation improves model development and deployment. It then addresses a key challenge in real-world ML systems: handling large datasets that cannot be stored directly in Git. This is solved using tools like DVC (Data Version Control).
You will learn how to track machine learning models using Git and GitHub Actions, enabling automated workflows for training, testing, and evaluation. The series also introduces advanced CI workflows, including running jobs on GPU-enabled environments and integrating R-based data science pipelines.
A major focus is automated testing for machine learning systems, including behavioral testing to ensure model reliability beyond traditional accuracy metrics. The course also explores experiment tracking using DVC, helping you manage multiple training runs and compare results effectively.
Later sections dive into GitOps for machine learning, showing how notebooks can be transformed into reproducible pipelines. The series concludes with best practices for end-to-end ML projects, combining version control, CI/CD, and experiment tracking into a unified production workflow.
By the end, you will understand how to build robust ML systems using Git, DVC, and GitHub Actions in real-world production environments.
