7 FREE Books to Learn About Building AI Systems

When someone asks me, "Can you build an AI application just by vibe coding?" my answer is yes, you probably can. But building an application that works is one thing. Building an AI system that can handle a million requests without becoming too slow or too expensive is a different challenge.

There is a lot more to AI engineering than making sure your model gives the right answer or completes the task. You need to think about inference latency, GPU memory, deployment, context management, and what happens when your application starts getting more traffic than you expected. If you're building AI agents, things get even more complicated.

There are some good courses that cover these concepts too, but I'd recommend reading books in depth if you really want to build your fundamentals. You don't necessarily have to pay for them either. I've gathered 7 FREE books that I feel are worth reading. Some are older and you may have already heard of them, while others are newer. So, let's get started.

1. Machine Learning Systems Design by Chip Huyen

This book is one of the classics and is good for beginners, so I'd start with this one. It treats machine learning as an end-to-end problem and covers its four main stages: 1) project setup, 2) preparing data pipelines, 3) modeling (selecting, training, and debugging), and 4) serving (testing, deploying, and maintaining). Each stage includes links to practical resources and case studies from machine learning engineers at large tech companies. Another good thing is that it has 27 system design questions at the end for practice. A lot has changed since 2019, so it doesn't go deep into large language model (LLM) infrastructure, but it's still useful for understanding how machine learning systems fit together.