AI Engineering: Building Applications with Foundation Models 1st Edition
by Chip Huyen
Key Highlights
- •🚀 Focuses on practical engineering for building applications with foundation models.
- •🔄 Covers the complete AI application lifecycle: design, data, deployment, MLOps, and maintenance.
- •👩💻 Authored by Chip Huyen, a leading expert in MLOps and production AI systems.
Description
Ready to move beyond AI theory and actually build things with powerful foundation models? Grab your copy of AI Engineering: Building Applications with Foundation Models! This essential guide by Chip Huyen is your roadmap to navigating the exciting, sometimes chaotic, world of modern AI application development. It's the practicle AI engineering book you need right now.
Who is this book for? This book is gold for software engineers, machine learning practitioners, data scientists, and even tech leads or managers who want to understand how to levarage large foundation models (like GPT, BERT, and others) in real-world products. If you're aiming to build, deploy, and maintain robust AI systems, this is definately for you. It's also great for students wanting a serious look into professional AI engineering.
What problem does this book solve? Feeling lost translating cool AI models into reliable, scalable applications? This book tackle exactly that. It bridges the often huge gap between theoretical machine learning knowledge and the practical engineering needed to make AI work in production. It helps you understand the unique challenges posed by foundation models and how to overcome them, covering the entire lifecycle from ideation to monitoring.
What will you gain from reading it? You'll get hands-on knowledge of the tools, techniques, and best practices essential for AI engineering today. Learn system design patterns for AI applications, understand data pipelines, deployment strategies, MLOps specific to foundation models, and how to ensure your AI systems are reliable and maintainable. This book help you build real stuff, not just toy projects.
Why is it worth reading? Chip Huyen is a leading voice in production ML and MLOps, bringing invaluable real-world experience. This 1st edition provides up-to-date insights into the fast-evolving field of foundation models. It's not just theory; it’s packed with actionable advice to make you a more effective AI engineer or builder. Stay ahead of the curve and learn how to build the next generation of AI applications.
Don't just learn about AI, start building with it! Download the PDF now and level up your AI engineering skills!
FAQ
Is this book mostly theoretical or practical?
It's highly practical! While it covers necessary concepts, the main focus is on the engineering aspect – how to actually design, build, deploy, and maintain AI applications using foundation models in real-world scenarios.
What background do I need to understand this book?
A solid understanding of programming (like Python) and basic machine learning concepts would be very helpful. It's aimed more towards engineers and practitioners rather than absolute beginners to both programming and AI.
Does the book cover specific foundation models like GPT-4 or Claude?
It focuses more on the general principles and engineering practices for working with *any* large foundation model, rather than deep-diving into the specifics of just one or two. The techniques are broadly applicable.
Is MLOps for foundation models covered?
Yes, absolutely. The book addresses the entire lifecycle, including the operational aspects (MLOps) specific to deploying and managing applications built on foundation models, which have unique challenges compared to traditional ML.
How up-to-date is the information, given how fast AI changes?
Being the 1st edition from a leading expert like Chip Huyen, it contains current best practices and insights relevant to today's foundation model landscape. While AI evolves fast, the core engineering principles discussed have lasting value.
Can I use this book if I'm a project manager, not a hands-on coder?
Yes, technical managers and project leads will find it very useful for understanding the complexities, timelines, and requirements involved in building applications with foundation models, helping them manage projects more effectively.
Reader Reviews
About the Author
Chip Huyen is a highly respected computer scientist and writer known for her expertise in Machine Learning systems and MLOps. With experience working at companies like NVIDIA, Snorkel AI, and Netflix, she bridges the gap between research and real-world application. Chip teaches Machine Learning Systems Design at Stanford University and is a prominent voice shaping the conversation around practical, production-ready AI.