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.

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.

Hands on Labs to prepare you for a career in Linux or system engineering. Built by the Professional Linux User's Group to focus on exactly what the professional needs in the enterprise.

Unlock the Secrets of MBSE Craftsmanship: Transform Your Organization's Engineering Culture and Excel in Model-Based Systems Engineering. Discover the Art and Philosophy Behind True Mastery.

This book provides a guided tour of ML techniques utilized in process industry for plant health management. Step-by-step instructions, supported with industrial-scale process datasets, show how to develop ML-based solutions for equipment condition monitoring, plantwide monitoring, and predictive maintenance solutions. Also available at Google Play 

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.”

"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

A practical engineer’s guide to feedback, stability, PID tuning, and state space control—with real-world examples and Python simulations.

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 provides a comprehensive coverage of ML methods that have proven useful in process industry for dynamic process modeling. Step-by-step instructions, supported with industry-relevant case studies, show how to develop solutions for process modeling, process monitoring, etc., using classical and modern methods. Also available at Google Play 

This book provides a guided tour along the wide range of ML methods that have proven useful in process industry. Step-by-step instructions, supported with real process datasets, show how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, soft sensing, and process control. Also available at Google Play.

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.

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.

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.