Books

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.

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.

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.

Kalman Filters Made Easy is a clear, intuitive introduction to one of engineering’s most powerful tools for dealing with noisy, imperfect, real‑world data. Instead of drowning you in equations, this book builds deep understanding through real examples — from drifting GPS signals to unpredictable sensor timing — and shows how engineers combine noisy measurements and imperfect models to estimate what’s really happening. If you work with robotics, drones, autonomous systems, or sensor‑driven devices, this guide gives you the mental models you need to design reliable systems in an uncertain world. This reflects the book’s core message: “The world gives us noisy, imperfect measurements. We want clean, reliable, accurate information. The filter is the bridge between the two.”

A true classic, not just in computing but in the broad area of scholarship. It is partly about the philosophy and mechanisms of science; partly about designing things so they work but mostly it is about how humans view the world and create things that match that view. This book will still be worth reading for a long time to come. - Charles Ashbacher

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 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 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.

A practical implementation framework covering 22 essential data center operational domains. Assess gaps, establish priorities and build a controlled improvement roadmap.

This practical guide explains how to calculate, interpret, and improve OEE using real-world examples, enabling engineers, supervisors, and Lean practitioners to increase productivity, reduce downtime, and achieve world-class manufacturing performance.

Build optimization models that remain useful when forecasts are wrong. This practical guide connects uncertainty sets, robust counterparts, duality, budgets of uncertainty, and modern distributionally robust optimization with real operational applications and implementation advice.

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.

Move beyond AI demos and build agents that can actually run IT operations. This practical guide takes you from MCP fundamentals to production-ready monitoring, incident response, remediation, Kubernetes operations, security and multi-agent systems, with complete runnable code and a strong focus on safe autonomy.