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by Chip Huyen
Chip Huyen's AI Engineering steps right into the practical heart of building with today's powerful AI models. This isn't a theoretical deep dive, but a clear, accessible guide for anyone eager to turn groundbreaking AI into working applications, even if you're relatively new to the space. The book feels like a conversation with a knowledgeable friend, breaking down the new AI stack and showing you how to navigate models, datasets, and crucial evaluation. It empowers you to move from simple techniques to sophisticated methods like RAG and agent design. If you're a developer or engineer looking to leverage the current wave of AI without getting bogged down in academic esoterica, and you want to truly understand the "how-to" of deploying AI solutions, this book offers a direct path forward.
If you found yourself nodding along with Chip Huyen's pragmatic approach to building AI applications and appreciated her clear, empowering guidance, then our curated recommendations will resonate deeply. Each of these books shares that same engineering-first mindset, focusing on the practical "how-to" of bringing machine learning and AI systems to life. Whether you're interested in the end-to-end process of ML engineering, hands-on application development, or specific design patterns for common problems, these titles offer further accessible, problem-solution insights to expand your toolkit and build robust AI solutions.
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by Chip Huyen
This book is by the same author and focuses on practical aspects of building and deploying machine learning systems, offering insights into the engineering challenges similar to 'AI Engineering'.
Emmanuel Ameisen provides a hands-on approach to designing and implementing machine learning applications, which aligns with the pragmatic and engineering-focused themes found in 'AI Engineering'.
This book covers the end-to-end process of creating machine learning applications, emphasizing engineering practices, making it a good complement to the themes discussed in 'AI Engineering'.
by Jeremy Howard and Sylvain Gugger
While focusing on deep learning, this book provides practical, hands-on experience in building AI systems, similar to the practical approach found in 'AI Engineering'.
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by Valliappa Lakshmanan, Sara Robinson, and Michael Munn
This book offers design patterns to solve common problems in machine learning, aligning with the engineering focus and problem-solving themes in 'AI Engineering'.
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