Systems Engineering

Learn SysML v2 with the ultimate guide for all skill levels in MBSE. Authored by insiders, it's your key to unlocking the full potential of system modeling and a passport to mastering your MBSE.

"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

Pain-Free MBSE is a practical guide to applying SysML without the unnecessary pain. Too often, MBSE gets a bad reputation for being slow, rigid, or overly complex. This book changes that. Using the Lunar Lander as a running example, it exposes high-pain modeling practices and introduces simpler, more effective alternatives. You'll learn how to apply the Value-to-Pain Ratio (VPR) to streamline your process, improve collaboration, and build models that work in the real world. Whether you’re a systems engineer, software developer, or decision-maker, Pain-Free MBSE helps you model with confidence — and without the headaches.

The book highlights the significance of software in systems engineering and uses AI as a subject matter expert. It presents a comprehensive example that covers SysML modeling, including requirements, use cases, logical/ physical architecture, and parametric simulation. It then continues into software, leveraging AI's code generation capabilities to produce software including microcontroller, UI, and DMBS code. It introduces a variety of personas and agents that can help engineers communicate with AI about systems and software engineering. The book also introduces SysML v2, focusing on the new language model and exploring AI's ability to generate models via code generation. Perhaps most importantly, it provides a straightforward roadmap for hardware/software co-design, accelerated at every step by AI. Whether you're a systems or software engineer, or just interested in how to use AI for engineering, AI Assisted MBSE with SysML will prove to be a valuable guide.

Linux is one of the best decisions you can make for your computer — but knowing where to start is the hard part. This guide walks you through everything: choosing a distribution, installing it, and using it confidently every day. It was written by Jay LaCroix of Learn Linux TV, for complete beginners. No prior experience required.

SYSMOD is an MBSE toolbox for pragmatic modeling of systems. It is well-suited to be used with SysML. The book provides a set of methods with roles and outputs. Concrete guidances and examples show how to apply the methods with SysML.

Updated, useful reference for those involved in plant design, covering all aspects of plant layout in broad detail

Local Intelligence shows you how to run large language models entirely on your Mac with Apple Silicon. Learn to use tools like Ollama, MLX, and llama.cpp, understand quantization, and build real local AI applications with open-source code.

This is the classic volume on every variety of technical review of programs, designs, tests, documentation, plans, requirements, ...

Go beyond writing code and learn what it takes to build software that lasts. Fundamentals of Software Engineering walks you through requirements, architecture, testing, deployment and production using practical trade-offs and a real-world case study. Follow TaskFlow from its first requirement to a system built to scale, evolve and survive the real world.

Model Predictive Control sounds complicated—until you see how naturally it fits the way you already think. Look ahead. Plan. Adjust. Repeat. This book turns a powerful control method into something intuitive, practical, and surprisingly simple.

This book familiarizes readers with the world of LLM and agentic AI, and helps them quickly gain a working-level knowledge of building useful agentic AI solutions for process industry operations. With no prerequisites required, practical demo applications, and a hands-on approach adopted throughout, this book makes advanced AI technologies accessible to process engineers and data scientists alike. It aims to help process data scientists and engineers take their first confident steps into Agentic AI world, understand the full picture, and build a strong enough foundation to keep learning and building on their own. Also available here.

This short book introduces process data scientists to knowledge graphs and GraphRAG, a powerful combination that overcomes the limitations of vanilla RAG when answering the kind of cross-document, relationship-heavy questions that process engineers usually care about. With a hands-on, application-driven approach, the book walks readers through the complete pipeline: designing an ontology for plant data, using LLMs to extract entities and relationships from incident reports, building and querying the graph in Neo4j, and finally assembling everything into a demo web application.

This book is a quick foray into the world of deep learning-based computer vision and abnormal equipment sound detection. The readers are introduced to the ease with which powerful equipment and product quality monitoring solutions can be built using sound and visual data.

Go beyond the surface of Apple binaries and uncover the inner workings of Mach-O. From file structures and dynamic linking to code signing and runtime execution, this book delivers the technical depth, practical tools and implementation details needed to analyze, understand and master executable binaries across the Apple ecosystem.