AI Engineering
Building Applications with Foundation Models
By: Chip Huyen
About This Book
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).
AI Overview
Overview of "AI Engineering" by Chip Huyen
"AI Engineering" by Chip Huyen is a detailed guide that bridges the technical and practical aspects of building AI applications. The book focuses on the shift from traditional machine learning to AI engineering, emphasizing a more product-focused approach. Here are the key themes and critical reception:
Key Themes
Transition from ML to AI Engineering:
- The book highlights the evolution from machine learning (ML) to AI engineering, focusing on the broader engineering and product development aspects of AI applications.
Foundation Models:
- It delves into the use of foundation models, including large language models (LLMs) and large multimodal models (LMMs), and how to adapt them for specific applications.
Technical and Practical Aspects:
- The book ties together the technical side of AI models with the practical side of setting up and developing applications, making it a comprehensive resource for both beginners and experienced engineers.
Finetuning and Inference Optimization:
- It goes in-depth into topics like finetuning and inference optimization, which are crucial for optimizing AI performance but can be complex for some readers.
Prompt Engineering and RAG:
- The book emphasizes the importance of prompt engineering and discusses the strategies for using Retrieval-Augmented Generation (RAG) models, highlighting their effectiveness despite rumors about their decline.
AI Evaluation and Agent Development:
- It covers the challenges of AI evaluation and provides insights into building and evaluating AI agents, including strategies for detecting and mitigating hallucinations in AI outputs.
Data Quality and Model Optimization:
- The book addresses questions related to data quality, model validation, and optimization, including how to make models faster, cheaper, and more secure.
Feedback Loops for Continuous Improvement:
- It discusses the importance of creating feedback loops to continually improve AI applications, ensuring they remain relevant and effective over time.
Critical Reception
Positive Reception:
- The book has been praised for its comprehensive approach, tying together various aspects of AI engineering in a way that is both accessible and insightful. Reviewers appreciate how it emphasizes product thinking, which is often overlooked in technical books.
- The detailed case studies and extensive references make the book a valuable resource for both beginners and experienced engineers. The emphasis on fundamentals rather than specific tools or APIs ensures that the content remains relevant over time.
Book Review Summary:
- A book review on Hippocampus's Garden highlights the book's ability to make complex AI concepts understandable. The reviewer appreciates the practical focus and the detailed explanations of advanced topics like finetuning and inference optimization. The book is recommended for anyone looking to build AI applications, from beginners to experienced engineers.
Plot Summary
The book does not have a traditional narrative plot but rather a structured guide that covers various aspects of AI engineering. It starts by explaining the transition from ML to AI engineering and then delves into the specifics of working with foundation models. It covers topics such as prompt engineering, RAG strategies, AI evaluation, agent development, data quality, model optimization, and the importance of feedback loops for continuous improvement.
In summary, "AI Engineering" by Chip Huyen is a seminal work that provides a comprehensive framework for building AI applications. Its emphasis on product-focused work, detailed case studies, and practical advice make it a valuable resource for anyone involved in AI engineering.