This MLOps Coding Course focuses on the fundamental tools and development practices required for machine learning engineers and MLOps professionals. Rather than jumping directly into model deployment, the course builds a strong foundation in the software engineering skills needed for production machine learning.
The course begins with an overview of MLOps systems and the role of engineering best practices in machine learning projects. You will learn how to configure a professional development environment and understand the tools commonly used in modern ML workflows.
Topics include Python setup and project management, along with the uv package manager for creating and maintaining reproducible Python environments. The course also demonstrates how to structure projects efficiently for scalability and collaboration.
Version control is another major focus. You will learn Git fundamentals, GitHub workflows, and how to manage code changes effectively in team-based environments. The course also introduces Visual Studio Code (VS Code), one of the most widely used editors for machine learning and software development.
In addition, you will explore notebook workflows, project organization, and import management techniques that help maintain clean and maintainable codebases.
By the end of this course, you will have the essential development skills and tooling knowledge required to begin building reliable machine learning and MLOps projects using modern engineering practices.
