Foundations of MLOps
This part lays the groundwork for your MLOps journey, guiding you through the transition from DevOps to MLOps while establishing core principles, practices, and workflows. You will learn how to manage machine learning (ML) workspaces, prepare and track data, design experiments, and implement training pipelines using cloud-native tools. By focusing on reproducibility, reusability, and automation, this section equips you with the practical knowledge needed to efficiently develop and manage ML models, ensuring that your solutions are robust, scalable, and ready for production.
This part has the following chapters:
- Chapter 1, Understanding DevOps to MLOps
- Chapter 2, Training and Experimentation
