AI Engineering: Building Applications with Foundation Models

Kirjeldus

AI Engineering: Building Applications with Foundation Models – 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. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.

AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You’ll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.

Understand what AI engineering is and how it differs from traditional machine learning engineering
Learn the process for developing an AI application, the challenges at each step, and approaches to address them
Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work
Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them
Choose the right model, dataset, evaluation benchmarks, and metrics for your needs

Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She’s the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.

AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O’Reilly).

Who is this book for?

AI Engineering: Building Applications with Foundation Models is a strong choice for developers, product builders, technical leaders, and readers following the business impact of modern AI. It suits readers who want a book that is clear, useful, and grounded in a recognisable real-world context rather than empty hype.
It also works well for people who are browsing English language books online and want a title with lasting value, whether for personal growth, study, work, or a thoughtful gift.

Main themes of the book?

The main themes of the book include AI systems, implementation, model capabilities, workflows, product development, and technological change. A practical, modern technical guide to designing and shipping AI applications built on foundation models.
Across the book, readers can expect practical insight, a consistent point of view, and ideas that connect well with modern readers searching for high-quality English nonfiction.

Why this book stands out from other artificial intelligence books?

What helps this title stand out from other artificial intelligence books is its combination of clarity, relevance, and substance. It gives readers a more usable and memorable experience than books that feel overly generic, making it a strong pick for readers who want both depth and readability.

Available in Estonia, Finland, Latvia and Lithuania

Available in Estonia, Finland, Latvia and Lithuania. Fast delivery. Secure ordering, prices in euros, and local customer support.

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