"AI Engineering: Building Applications with Foundation Models" is a practical and insightful book authored by Chip Huyen, a well-known figure in machine learning and AI engineering. This book provides a comprehensive guide to leveraging foundation models, such as large language models (LLMs) and generative AI, to build scalable, impactful AI applications for real-world use cases.
What Are Foundation Models?
Foundation models are pre-trained AI models (like GPT, BERT, and Stable Diffusion) that are designed to be adaptable for a wide variety of downstream tasks, including natural language processing, computer vision, and more. This book focuses on the practical application of these powerful models.
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.
Core Focus of the Book
The book emphasizes:
AI Engineering Principles: It explores the discipline of AI engineering, which combines software engineering, machine learning, and DevOps to develop production-ready AI systems.
End-to-End Application Development: The book provides a roadmap for designing, developing, and deploying AI solutions using foundation models, including the integration of APIs and pipelines.
Evaluation and Monitoring: Chip Huyen also sheds light on techniques to evaluate the performance and fairness of AI models in dynamic and open-ended scenarios.
Adaptability and Scalability: It highlights how foundation models can be adapted for custom tasks and scaled to meet enterprise needs.
Who Is It For?
The book is targeted at:
AI practitioners and engineers looking to implement foundation models in their work.
Developers aiming to transition from machine learning prototyping to scalable production systems.
Students and professionals interested in understanding the practicalities of AI application development.
Why Is This Book Unique?
Focus on Foundation Models: It bridges the gap between the theoretical understanding of foundation models and their practical application in industry.
Real-World Insights: The author draws from her extensive experience building AI systems at scale, offering actionable advice and best practices.
Comprehensive Topics: It covers everything from technical aspects like pipeline design and API integration to broader themes such as ethical AI and responsible model usage.
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