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) .
