Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems

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. AI and ML systems have a lot of moving parts, from language libraries and application frameworks, to workflow and deployment infrastructure, to LLMs and other advanced models. A well-designed internal development platform (IDP) gives developers a defined set of tools and guidelines that accelerate the dev process, improving consistency, security, and developer experience.

This book demonstrates how to build an effective IDP for ML and AI applications. Each chapter illuminates a vital part of the ML workflow, including setting up orchestration pipelines, selecting models, allocating resources for training, inference, and serving, and more. As you go, you’ll create a versatile modern platform using open source tools like Kubeflow, MLFlow, BentoML, Evidently, Feast, and LangChain.

The book has four parts, covering 13 chapters. Part 1 (Chap. 1~3) explains what MLOps entails and how to build the infrastructure foundation for ML systems. Part 2 (Chap. 4~6) focuses on building the core platform capabilities that transform ad hoc ML processes into production-ready systems. Part 3 (Chap. 7~11) demonstrates MLOps principles in action through two complete, real-world projects. Finally, Part 4 (Chap. 12, 13) extends MLOps to generative AI through LLMOps, building production RAG systems with vector databases, prompt management, and safety controls, and then hardening them for enterprise deployment with testing, guardrails, and cost optimization.

It is for data scientists and software engineers who want to move beyond Jupyter Notebooks to production ML systems.