You will find that this textbook has a logical structure that makes studying easier.
Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives.
"AI Engineering: Building Applications with Foundation Models" by Chip Huyen is a comprehensive textbook focusing on the application of artificial intelligence in various fields such as enterprise applications, business intelligence, machine theory, and natural language processing. Published by O'Reilly Media, this trade paperback book covers essential topics related to computer engineering, making it a valuable resource for students and professionals in the field. With a publication year of 2025, this 532-page book provides insights and guidance on how to build practical AI applications using foundation models, making it an essential addition to any AI enthusiast's library.
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You will find that this textbook includes a wide range of perspectives on the topic.
The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.
This book explores the intersection of artificial intelligence and engineering, offering practical insights and methodologies to leverage AI in various applications. .
ISBN-13: 9781098166304, 978-1098166304. Author(s): Chip Huyen. Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products.
Designing Machine Learning SystemsAn Iterative Process for Production-Ready Applications Author(s): Chip Huyen Format: Paperback Publisher: O'Reilly Media, United States Imprint: O'Reilly Media ISBN-13: 9781098107963, 978-1098107963 Synopsis Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
This book teaches you how to design, build, and deploy production-ready machine learning systems. It covers essential topics such as data pipeline, model training, and deployment strategies, providing practical insights and real-world examples. .
