Hao B. Build a Machine Learning Platform (From Scratch) (MEAP v6) 2025

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Hao B. Build a Machine Learning Platform (From Scratch) (MEAP v6) 2025

Textbook in PDF format Get your Machine Learning models out of the lab and into production! Delivering a successful Machine Learning project is hard. Build a Machine Learning Platform (From Scratch) 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 Build a Machine Learning Platform (From Scratch) 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 Build a Machine Learning Platform (From Scratch) 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 book Build a Machine Learning Platform (From Scratch) teaches you to set up and run a production-quality Machine Learning system using open source tools. Chapter-by-chapter, you’ll assemble a delivery pipeline for an image classifier and a recommendation system, learning best practices as you go. You’ll get hands-on experience with the most important parts of the Machine Learning workflow, including orchestrating pipelines; model training, inference, and serving; and monitoring and explainability. Soon, you’ll be deploying models that are fast to production and easy to maintain and scale. Preface Getting started with MLOps and ML engineering What is MLOps? Building applications on Kubernetes Designing reliable ML systems Orchestrating ML pipelines Productionizing ML models Data analysis and preparation Model training and validation: Part 1 Model training and validation: Part 2 Model inference & serving Monitoring and explainability Designing LLM-powered systems Production LLM system design Appendix A. Installation and setup Appendix B. Basics of YAML