djvu Practical AI for Engineers: Machine Learning, Automation, and Industrial Applications

AI models are easy to demonstrate. Engineering AI systems that remain useful, reliable, and safe in real industrial operations is the harder challenge.

Practical AI for Engineers bridges the gap between machine-learning theory and the realities of engineering deployment. Designed for engineers, engineering students, technologists, and technical leaders, this applied guide follows the complete industrial AI lifecycle—from identifying a worthwhile use case to commissioning, monitoring, governing, and maintaining an AI-enabled system.

Rather than treating machine learning as an isolated modeling exercise, the book connects algorithms to physical processes, sensors, industrial data, automation architecture, operational decisions, cybersecurity, human oversight, and business value.

Inside, you will learn how to:

• Identify where AI, conventional automation, optimization, simulation, or rules provide the strongest engineering solution
• Build trustworthy datasets from sensors, controllers, historians, maintenance records, and manufacturing systems
• Develop and evaluate regression, classification, tree-based, neural-network, anomaly-detection, forecasting, and computer-vision models
• Apply predictive maintenance, condition monitoring, remaining-useful-life concepts, and automated inspection
• Integrate model outputs with PLC, SCADA, historian, MES, and industrial communication workflows
• Deploy AI across edge, on-premises, and cloud environments
• Build reproducible MLOps pipelines for versioning, testing, monitoring, rollback, drift detection, and change control
• Address industrial AI safety, cybersecurity, governance, explainability, and human decision authority
• Use generative AI and engineering agents with controlled context, tools, permissions, and traceability
• Carry an industrial AI project from engineering justification through final commissioning

A recurring industrial case study connects the chapters, while worked examples, Python-based exercises, project gates, model cards, dataset sheets, assurance templates, assessment rubrics, and commissioning checklists turn concepts into engineering practice.

Whether you work in manufacturing, mechanical systems, electrical engineering, process industries, reliability, automation, robotics, energy, or industrial operations, Practical AI for Engineers provides a disciplined framework for moving beyond experimental models toward AI systems that can be justified, tested, integrated, monitored, and responsibly operated.