·Who This Book Is For

·This book is for anyone who wants to leverage foundation models to solve real-world problems. This is a technical book, so the language of this book is geared toward technical roles, including AI engineers, ML engineers, data scientists, engineering managers, and technical product managers. This book is for you if you can relate to one of the following scenarios:

·You’re building or optimizing an AI application, whether you’re starting from scratch or looking to move beyond the demo phase into a production-ready stage. You may also be facing issues like hallucinations, security, latency, or costs, and need targeted solutions.

·You want to streamline your team’s AI development process, making it more systematic, faster, and reliable.

·You want to understand how your organization can leverage foundation models to improve the business’s bottom line and how to build a team to do so.

·You can also benefit from the book if you belong to one of the following groups:

·Tool developers who want to identify underserved areas in AI engineering to position your products in the ecosystem.

·Researchers who want to better understand AI use cases.

·Job candidates seeking clarity on the skills needed to pursue a career as an AI engineer.

·Anyone wanting to better understand AI’s capabilities and limitations, and how it might affect different roles.

·I love getting to the bottom of things, so some sections dive a bit deeper into the technical side. While many early readers like the detail, it might not be for everyone. I’ll give you a heads-up before things get too technical. Feel free to skip ahead if it feels a little too in the weeds!

·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.

·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 Engineeringbuilds upon and is complementary toDesigning Machine Learning Systems (O'Reilly).