Preface

This book teaches computer science the way it’s practiced: concept first, then the working system that proves it. Each chapter develops a topic in narrative — the problem that motivates it, the mental model that makes it click, and the engineering tradeoffs that govern real systems — and then points you to a complete, runnable implementation in the companion Applied CS projects.

The prose carries the why and the shape of each idea; the heavy, production-sized code lives in the projects, linked from each chapter as “Build it →”. You can read a chapter for its ideas alone, or follow the link and run the system that embodies them.

The book is organized into six parts that mirror the curriculum:

Part I — Cross-Language Foundations: the concepts every language has to answer — concurrency, type systems, memory and resource management, error handling, testing, and performance — taught once, comparatively across Python, TypeScript, Go, Java, C++, and Rust.
Part II — Language Field Guides: each language’s distinctive idioms and ecosystem, building on the foundations rather than re-teaching them.
Part III — Data Engineering: pipelines, storage, streaming, and data quality.
Part IV — Machine Learning Engineering: training systems, serving, and ML infrastructure.
Part V — Cross-Cutting Concerns: observability, security, CI/CD, and cost.
Part VI — Infrastructure: how the systems above are containerized, orchestrated, and measured.

A note on scope

This is the systems-and-software companion to the curriculum; the AI-engineering material (LLMs, RAG, agents, inference) is covered in depth in the separate book, AI Engineering: Building Production-Ready LLM Applications, and is deliberately not duplicated here. Where a Machine Learning Engineering chapter brushes that boundary, it stays on the systems side and points to the companion book for the modelling theory.

How to read this book

Chapters open with a scenario, not a definition. Read for the Core Insight and Mental Model first; the implementation details land more easily once you know why a technique exists. Code in the prose is illustrative — the full, tested version is always one click away in the linked project.

Practical exercises are graded in three levels of increasing difficulty: Level I (foundational), Level II (intermediate), and Level III (advanced).

# Preface {.unnumbered} This book teaches computer science the way it's practiced: concept first, then the working system that proves it. Each chapter develops a topic in narrative — the problem that motivates it, the mental model that makes it click, and the engineering tradeoffs that govern real systems — and then points you to a complete, runnable implementation in the companion [Applied CS projects](https://github.com/jchu0/applied-cs-projects). The prose carries the *why* and the shape of each idea; the heavy, production-sized code lives in the projects, linked from each chapter as **"Build it →"**. You can read a chapter for its ideas alone, or follow the link and run the system that embodies them. The book is organized into six parts that mirror the curriculum: - **Part I — Cross-Language Foundations**: the concepts every language has to answer — concurrency, type systems, memory and resource management, error handling, testing, and performance — taught once, comparatively across Python, TypeScript, Go, Java, C++, and Rust. - **Part II — Language Field Guides**: each language's distinctive idioms and ecosystem, building on the foundations rather than re-teaching them. - **Part III — Data Engineering**: pipelines, storage, streaming, and data quality. - **Part IV — Machine Learning Engineering**: training systems, serving, and ML infrastructure. - **Part V — Cross-Cutting Concerns**: observability, security, CI/CD, and cost. - **Part VI — Infrastructure**: how the systems above are containerized, orchestrated, and measured. ::: {.callout-note} ## A note on scope This is the systems-and-software companion to the curriculum; the AI-engineering material (LLMs, RAG, agents, inference) is covered in depth in the separate book, [*AI Engineering: Building Production-Ready LLM Applications*](https://jameshu.io/books/ai-engineering), and is deliberately not duplicated here. Where a Machine Learning Engineering chapter brushes that boundary, it stays on the systems side and points to the companion book for the modelling theory. ::: ## How to read this book Chapters open with a scenario, not a definition. Read for the **Core Insight** and **Mental Model** first; the implementation details land more easily once you know *why* a technique exists. Code in the prose is illustrative — the full, tested version is always one click away in the linked project. Practical exercises are graded in three levels of increasing difficulty: **Level I** (foundational), **Level II** (intermediate), and **Level III** (advanced).