Ai Engineering Building Applications With Foundation Models
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AI Engineering
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 Engineering
AI Engineering: Building the Future with Foundation Models AI is no longer the exclusive domain of research labs. Recent breakthroughs, especially the rise of "model-as-a-service," have democratized access to powerful AI capabilities. This book is your guide to AI Engineering: the practical discipline of building real-world applications using readily available foundation models. It's a field that goes beyond traditional machine learning, demanding a new skillset and a new way of thinking. Forget training models from scratch. AI Engineering is about leveraging pre-trained giants – powerful foundation models that can understand language, generate images, and much more. This book will show you how to harness their power, even if you don't have a PhD in AI. We'll start by defining AI Engineering, contrasting it with traditional ML, and introducing the modern "AI stack" – the infrastructure and tools that make it all possible. Crucially, we'll emphasize the importance of evaluation. As AI's power grows, so does the potential for unintended consequences. This book will equip you with the knowledge to build responsible AI, including cutting-edge techniques like using AI to evaluate AI ("AI-as-a-Judge"). You'll learn a practical framework for developing AI applications, starting with the fundamentals and progressing to advanced techniques. We'll cover: The AI Engineering Process: Understand the steps, challenges, and solutions involved in building an AI application from start to finish. Model Adaptation: Master techniques like prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning, agents, and dataset engineering – learn how and why they work. Deployment and Optimization: Conquer the practical hurdles of latency and cost, ensuring your application is both efficient and effective. Strategic Choices: Navigate the vast landscape of models, datasets, and benchmarks to make informed decisions for your specific needs. This book is your practical guide to building the next generation of AI-powered applications. It is useful for the engineers and the product managers.
AI Engineering
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).