The AI Engineering Bootcamp Book: Build, Ship, Share
Enjoy self-paced learning and your path to building production-grade AI applications with our proven AI Engineering principles.
Pre-order your copy on Amazon today ahead of the official launch in January 2027, or explore all of our purchase options.
- Barnes & Noble: The AI Engineering Bootcamp: Build, Ship, Share | Barnes & Noble®
- Walmart: The AI Engineering Bootcamp: Build, Ship, Share, (Paperback) - Walmart.com
- Books-a-Million: The AI Engineering Bootcamp : Build, Ship, Share
- Bookshop.org: The AI Engineering Bootcamp a book by Greg Loughnane, Christopher Brousseau, and Chris Alexiuk - Bookshop.org US
What You'll Get From this Book
AI Engineering can feel like an endless stream of new models, frameworks, tools, and techniques. This book gives you a way through the noise, starting with the business problem and case you're solving with AI. You won't learn every new AI tool. You will learn the foundational engineering patterns underneath them so you can adapt as the tools change.
By the end of the book, you should be able to:
- Understand the AI Engineering landscape and how modern AI applications are put together.
- Use prompting effectively instead of treating prompts as magic strings.
- Build retrieval systems and RAG applications that connect models to external knowledge.
- Understand agentic systems and the problems they are designed to solve.
- Evaluate AI systems instead of relying on whether a demo happens to look good.
- Make engineering tradeoffs around models, retrieval, agents, evaluation, and system design.
- Move from prototype toward production with a clearer understanding of what changes along the way.
- Build a portfolio of practical AI engineering experience rather than simply accumulating knowledge about AI.
- Develop the mental models that make it easier to learn whatever comes next.
The information you read here is useful long after today's tools have changed. When you're done, we want to make sure you know how to look at an AI problem and ask:
- What should I build?
- How should I build it?
- How do I know whether it works?
- What does it take to ship it?
Frequently Asked Questions
About the Book
This book is for anyone looking to understand and practice AI Engineering.
It is especially useful for software engineers, data scientists, machine learning practitioners, and other technical professionals who want to build AI-powered applications or transition into AI Engineering.
You don't need to already consider yourself an AI Engineer. In fact, that's exactly who much of this book was written for.
Experienced practitioners may also find value in returning to the fundamentals and seeing how the core patterns fit together.
Yes. but it's about more than writing code.
AI Engineering sits at the intersection of software engineering, machine learning, product thinking, and system design.
This book focuses on the practical patterns that connect those disciplines when you're building AI-powered applications.
You should come away with a working understanding of the core patterns behind modern AI applications, including prompting, retrieval, RAG, agents, and evaluations.
More importantly, you'll have a framework for thinking through questions like:
What should I build?
How should I build it?
How do I know whether it works?
What does it take to ship it?
The goal of our learning style isn't for you to memorize or be the expert on today's current tools. It's to develop the engineering judgment needed to work with whatever comes next.
Learning AI Engineering
No. You do not have to be an AI engineer or forward-deployed engineer, but you should be in a growing software engineering or data science role to best be able to apply what's in this book.
The book is designed as a starting point for people moving into AI Engineering from software engineering, data science, machine learning, or other technical disciplines.
You should be comfortable with basic programming concepts, but you do not need to already know every AI technique covered in the book.
No. This book focuses primarily on using foundation models to build AI applications, rather than pretraining foundation models from scratch.
You'll learn about the engineering patterns that sit around modern language models and make them useful in applications.
Yes, but reading about AI Engineering isn't the same as doing it.
The book gives you the technical foundation and vocabulary needed to start building AI systems. The most valuable next step is to apply what you learn to a real problem.
Build something.
Ship it.
Evaluate it.
Share what you learned.
That's where the learning compounds.
The disciplines overlap, but they tend to emphasize different parts of the AI system.
Traditional Machine Learning Engineering often focuses on building, training, deploying, and operating machine learning models.
AI Engineering, as discussed in this book, focuses more heavily on building applications and systems around foundation models—including prompting, retrieval, agents, evaluation, and the surrounding application architecture.
The boundaries continue to evolve. The point of this book is not to enforce a perfect taxonomy, but to give you useful mental models for building modern AI systems.
Technical Patterns and Tradeoffs
Yes. RAG is one of the foundational AI engineering patterns covered in the book. You'll explore how retrieval can connect language models to external information and support applications that need access to relevant context.
The broader focus is on understanding where retrieval fits, how it works alongside other patterns, and the engineering decisions involved in designing AI systems around real data.
Yes. The book explores agentic approaches as part of the evolving AI engineering landscape, including how AI systems can use tools and coordinate steps to accomplish tasks.
It also emphasizes the importance of understanding the complexity and tradeoffs involved in agent-based systems, rather than assuming that every problem needs an agent.
Yes. Evaluating AI systems is an important part of moving beyond prototypes. The book introduces evaluation as an engineering practice for understanding system behavior, identifying limitations, and improving performance.
You'll develop a foundation for thinking about how AI applications can be tested and assessed, especially when their outputs are variable and their behavior may change across different inputs.
The book focuses on transferable engineering patterns and concepts rather than requiring readers to build around a single model or framework.
AI tools and platforms continue to change. Understanding the underlying approaches helps you make informed decisions about which tools are appropriate for a particular use case and adapt as the ecosystem evolves.
Pre-Order the AI Engineering Bootcamp Book on Amazon Today
Note: The book is included with an enrollment in any AI Makerspace course. Start learning AI engineering here, or reinforce what you're building with our book.
The AI Engineering Bootcamp: Build, Ship, Share, brings you self-paced learning on how to build production AI.
Whether you're building your first AI application, getting promoted, or deploying enterprise AI systems in production, this book provides a solid roadmap for understanding, building, and improving modern AI solutions.
Launch Date: January 2027
Price: $65 USD
🏗️ Build. 🚢 Ship. 🚀 Share.