about this book
Most machine learning projects never make it to production. The challenge isn’t building models—it’s deploying them reliably, monitoring their performance, and maintaining them at scale. Machine Learning Platform Engineering teaches you how to build the complete infrastructure and workflows needed to operationalize ML systems, from experiment tracking to production deployment.
By the end of this book, you’ll have built a complete ML platform from the ground up. You’ll containerize and orchestrate ML workloads, automate training pipelines, deploy models as scalable APIs, and implement comprehensive monitoring—all using industry-standard tools such as Docker, Kubernetes, MLflow, and Kubeflow. The final chapters extend these practices to LLMs, showing you how to build and secure production Retrieval-Augmented Generation (RAG) applications.
Who should read this book?
This book is for data scientists and software engineers who want to move beyond Jupyter Notebooks to production ML systems. You should be comfortable with Python and have basic familiarity with ML concepts. No prior experience with Docker, Kubernetes, or machine learning operations (MLOps) tools is required—we’ll build everything from scratch. Experienced ML practitioners will benefit from the systematic approach to infrastructure and the modern LLMOps coverage in the final chapters.
What you’ll build
You’ll construct three complete systems: