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AI Engineering – Building Applications with Foundation Models (Paperback)
AI Engineering – Building Applications with Foundation Models (Paperback)

AI Engineering – Building Applications with Foundation Models (Paperback)

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Product Description

AI engineering is rapidly transforming how modern software applications are built, deployed, and scaled. AI Engineering: Building Applications with Foundation Models (1st Edition) is a practical, industry-focused guide designed for developers, machine learning engineers, and technical leaders working with large language models and foundation models. AI engineering principles in this book focus on moving beyond experimentation to building reliable, production-grade AI systems using modern architectures and tools.

AI engineering concepts are explained through real-world examples that show how to design, evaluate, and deploy applications powered by foundation models such as large language models. The book emphasizes system design, model selection, prompt engineering, orchestration, evaluation, and monitoring—critical skills for teams shipping AI-driven products. AI engineering practices outlined here help readers bridge the gap between research breakthroughs and scalable software solutions used in real businesses.

🤖 About the Book

AI engineering is presented as a discipline that blends software engineering, machine learning, and product thinking. This book provides a clear framework for building AI applications responsibly and efficiently using foundation models. Readers learn how to integrate models into applications, manage latency and cost, handle hallucinations, and evaluate outputs systematically. The content reflects current industry best practices rather than theoretical experimentation.

🎯 What You’ll Learn / Why Read

AI engineering skills gained from this book include designing AI-powered systems, working effectively with foundation models, and deploying applications safely in production environments. You will learn how to structure AI pipelines, apply prompt engineering techniques, evaluate model performance, and implement feedback loops. AI engineering guidance in this book is essential for developers building chatbots, copilots, recommendation systems, and intelligent automation tools. It is especially valuable for professionals transitioning from traditional software development into AI-enabled products.

✍️ Author Bio

Chip Huyen is a well-known AI researcher, engineer, and educator with extensive experience in machine learning systems and production AI. She has worked with leading technology companies and is recognized for her clear explanations of complex AI topics. Her work on AI engineering is widely followed by practitioners building real-world AI applications.

📦 Product Details

  • Title: AI Engineering: Building Applications with Foundation Models

  • Author: Chip Huyen

  • Edition: 1st Edition

  • Publisher: O’Reilly Media

  • Language: English

  • Format: Paperback

  • ISBN: 978-1098166304

  • Genre: Artificial Intelligence, Software Engineering, Machine Learning

  • Pages: Approx. 300+ pages

🛒 Why Buy from Nybookshub

AI engineering books from Nybookshub are guaranteed to be original, authentic paperback editions sourced from trusted publishers. Nybookshub specializes in global technical and professional titles used by developers and engineers worldwide. We offer secure packaging, reliable international shipping, and a curated catalog of cutting-edge technology books. When learning accuracy and up-to-date content matter, Nybookshub delivers confidence.

❓  FAQs

Q1. Is this book suitable for software developers new to AI?
Yes, AI engineering concepts are explained clearly for developers with basic programming knowledge.

Q2. Does this book focus on theory or practical implementation?
It focuses strongly on practical, production-ready AI engineering workflows.

Q3. Is this the official 1st edition paperback from O’Reilly?
Yes, Nybookshub supplies only genuine publisher-authorized editions.

  • ai engineering book

  • foundation models applications

  • large language models development

  • ai system design

  • machine learning engineering

  • prompt engineering guide

  • production ai systems

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