Textbook in PDF format Get your Machine Learning models out of the lab and into production! Delivering a successful Machine Learning project is hard. Machine Learning Platform Engineering makes it easier. In it, you’ll design a reliable ML system from the ground up, incorporating MLOps and DevOps along with a stack of proven infrastructure tools including Kubeflow, MLFlow, BentoML, Evidently, and Feast. In Machine Learning Platform Engineering you’ll learn how to Set up an MLOps platform Deploy machine learning models to production Build end-to-end data pipelines Effective monitoring and explainability A properly designed Machine Learning system streamlines data workflows, improves collaboration between data and operations teams, and provides much-needed structure for both training and deployment. In Machine Learning Platform Engineering you’ll learn how to design and implement a Machine Learning system from the ground up. You’ll appreciate this instantly-useful introduction to achieving the full benefits of automated ML infrastructure. about the technology 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. about the book Machine Learning Platform Engineering shows you 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. what's inside Set up an end-to-end MLOps/LLMOps platform Deploy ML and AI models to production Effective monitoring, evaluation, and explainability about the reader 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