The world of AI is exploding with opportunity, but many developers feel overwhelmed by the complexity. If you've been curious about building AI applications but don't know where to start, this book is your comprehensive guide to navigating the AI landscape with confidence.
Recent breakthroughs haven't just increased demand for AI products—they've lowered the barriers to entry dramatically. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can leverage. Whether you're a seasoned developer or just starting out, you can now build sophisticated AI applications using readily available foundation models.
This book provides a systematic approach to AI engineering that takes you from concept to deployment. You'll discover how to navigate the complex AI landscape, select the right models and datasets, and understand which evaluation benchmarks and metrics matter for your specific needs. The framework starts with simple techniques and progressively guides you toward more sophisticated methods, ensuring you build a solid foundation before tackling advanced concepts.
Learn the core tools that power today's AI applications through hands-on guidance on model adaptation techniques. You'll explore prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, agents, and dataset engineering. The book explains not just how these techniques work, but why they work in different scenarios, helping you make informed decisions about which approach to use for your specific application.
As AI adoption grows, so do the risks of catastrophic failures. This book addresses the critical importance of evaluation in AI development, including the rapidly growing AI-as-a-judge approach. You'll also learn to overcome common bottlenecks in latency and cost when serving foundation models, ensuring your applications are both powerful and efficient.
Written by Chip Huyen, an experienced AI professional who has worked with NVIDIA, Snorkel AI, and taught Machine Learning Systems Design at Stanford, this book builds upon her bestselling work in designing machine learning systems. Whether you're looking to enhance your career, build innovative products, or simply understand the AI revolution, this comprehensive guide provides the knowledge and practical insights you need to succeed in the exciting world of AI engineering.
