Books

AI engineering is becoming one of the most valuable and in-demand areas of software development, and Python, LangChain, and LangGraph are core skills for building the systems behind it. Learn how to turn basic Python knowledge into production-grade backend and agentic AI applications—and move toward an engineering niche centered on building and controlling AI rather than competing with it.

Our brain builds models (you could think of them as apps) to represent what we learn.These models allow us act on what we've learned without thinking. Learn how to build better models (and how to improve them) and at the same time make learning an enjoyable process.

This 2025 edition of Docker Deep Dive is a true masterpiece. The best-selling Docker book is up-to-date with the hottest Docker technologies and the latest industry trends. This edition includes a brand-new chapter on Docker Model Runner for fast, private LLM-based apps. You won't find a better and more up-to-date book on Docker. Hand-crafted and kept up to date over the past 9 years by best-selling author Nigel Poulton.

Need a quick introduction to running an effective EventStorming session? This book is a handy quick start guide for anyone wanting to learn how to facilitate an EventStorming session.

Fifty RF‑101 Voodoos arrived in Vietnam. Forty‑seven were shot down in a year. The pilots who survived didn't fly faster aircraft — they cycled through the OODA loop faster than the missiles chasing them. Jeff Sutherland was one of them. He went on to co‑create Scrum. Now, six decades later, he is running AI agents through daily sprints at machine speed — and this book is the playbook for how to do it without getting shot down.

This book will help you with interview preparation for landing high-paying software engineering jobs in the financial markets industry – Hedge Funds, Banks, Algo Trading firms, HFT firms, Exchanges, etc. Questions that are comparable difficulty level to those asked at top financial firms.

This book bridges theory and practice in PD rating modeling, offering practical steps, real-world examples, and a focus on design. It enables readers to shape customized solutions for diverse institutions, transforming the landscape of credit risk modeling.

"If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book." —Cassie Kozyrkov, Chief Decision Scientist at Google "Foundational work about the reality of building machine learning models in production." —Karolis Urbonas, Head of Machine Learning and Science at Amazon

You know how to build frontend applications. But can you explain how you would design one when the requirements are vague, the constraints keep changing, and you only have 45 minutes?Frontend System Design Interviews gives you a practical framework for structuring that conversation, making trade-offs, and going deep on the problems that actually change the architecture.

Explore recipes for both classical quantitative finance approaches to modeling financial data, as well as modern machine learning and deep learning solutions

AI coding agents can move faster than Git’s staging-and-commit workflow.Juju-chu! shows how Jujutsu🐦‍⬛ gives you automatic commits, reliable undo, and a safer way to reshape messy AI-generated changes—while staying compatible with Git and GitHub. For developers who already use Git and want a calmer AI workflow.

The practical Windchill reference for administrators, developers and PLM consultants: 27 problem-to-fix cards, 25+ Java API recipes, admin routines and ready-to-use scripts.

Take your Delphi skills beyond the basics and learn how to build real-world web applications with DMVCFramework. From your first REST API to secure, scalable services, this practical guide covers the tools, patterns and techniques you need to build, test and deploy production-ready applications with confidence.

You don’t need massive compute or big-tech resources to build real AI. This hands-on guide shows you how to build and fine-tune your own small language model—from scratch—using accessible tools like Google Colab. Learn transformers step-by-step, train and align models on your own data, and deploy practical AI systems that run on consumer GPUs. If you’ve ever wondered “Can I build my own model?”—this book proves the answer is yes.

Happy-path architecture is a myth. When downstream dependencies degrade, naive retries trigger thundering herds, thread pools exhaust in seconds, and dual-writes silently corrupt financial balances. Surviving Distributed Systems is an unvarnished, production-tested field guide for senior backend engineers. Packed with architectural diagrams and modern Java 21 code, this book teaches you how to design, isolate, and harden high-concurrency systems so they survive catastrophic cascading failures.