Del 0 - What Every Engineer Should Know

What Every Engineer Should Know About Artificial Intelligence and Big Data

Nyhet

Häftad, Engelska, 2026

AvSatish Mahadevan Srinivasan,Raghvinder S. Sangwan

919 kr

Kommande

Recognizing the vast potential in analyzing big data through machine learning (ML) and artificial intelligence (AI) technologies, companies are acknowledging these technologies as essential for maintaining relevance. A prevailing trend is emerging towards the adoption of distributed open-source computing for storing big data assets and performing advanced ML/AI analytics to predict future trends and risks for businesses. This book offers readers an overview of the essentials of big data and ML/AI, while acknowledging that the field is extensive and evolving. Rather than focusing on theory, the book shares real-life experiences building AI and big data analytics systems of value to practitioners.• Features practical case studies on building big data and AI models for large scale enterprise solutions.• Discusses the use of design patterns for architecting AI that are safe, secure, and testable.• Covers an array of concepts including deep big data analytics, natural language processing, transformer architecture and evolution of ChatGPT, swarm intelligence, and genetic programming.Informed by the authors' many years of teaching ML, AI, and working on predictive data analytics/AI projects, this book is suitable for use by graduates, professionals, and researchers within the field of data science and engineers and scientists interested in learning more about these essential technologies.

Produktinformation

Utgivningsdatum2026-07-06
Mått156 x 234 x undefined mm
FormatHäftad
SpråkEngelska
SerieWhat Every Engineer Should Know
Antal sidor280
FörlagTaylor & Francis Ltd
ISBN9781032829852

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Satish M. Srinivasan is an Associate Professor of Information Science at Pennsylvania State University. He received his B.E. in Information Technology from Bharathidasan University, India, M.S. in Industrial Engineering and Management from the Indian Institute of Technology Kharagpur, and Ph.D. in Information Technology from the University of Nebraska at Omaha. Prior to joining Penn State Great Valley, he worked as a postdoctoral research associate at University of Nebraska Medical Center, Omaha. He teaches courses related to database design, data mining, data collection and cleaning, computer, network and web securities, and business process management. His research interests include data aggregation in partially connected networks, fault- tolerance, software engineering, social network analysis, data mining, machine learning, big data and predictive analytics, and bioinformatics.Raghvinder S. Sangwan earned his Ph.D. in Computer and Information Sciences from Temple University. He is a Professor of Software Engineering at Pennsylvania State University with expertise in analysis, design, and development of large-scale software-intensive systems, and the use of AI engineering to design and develop intelligent systems that are safe, secure, and trustworthy. His research focuses on the improvement of these practices, and he has taught related courses to engineers and project managers at many prestigious academic, government and industry organizations worldwide. He actively consults for Siemens Corporate Technology in Princeton, NJ and is affiliated as a visiting scientist with the Software Engineering Institute at Carnegie Mellon University. He also serves as an entrepreneurial coach and mentor to student and faculty entrepreneurial teams and is an instructor in the Mid-Atlantic NSF I-Corps program. He is an IEEE distinguished contributor and senior member of the ACM.

0. Front Matter. Part I. Foundations & Platforms, Automation & Data Quality at Scale. 1. Fundamental concepts in AI. 2. Big Data and Artificial Intelligence Systems. 3. Architecting Big Data pipelines. 4. Big Data Frameworks and Data Cleaning Strategies. 5. Building Automated Pipelines for Data Cleaning. Part II. Optimization & Search. 6. Swarm Intelligence. 7. Genetic Programming. Part III. Learning Systems. 8. Foundations on Machine Learning and Artificial Learning. 9. Reinforcement Learning. 10. Deep Reinforcement Learning. 11. Natural Language Modelling. 12. Transformer Architecture and Evolution of LLM’s. Part IV. Systems in the Real World. 13. Architecting Distributed AI Systems using Design Patterns. 14. Securing AI Systems. 15. AI System Safety in Practice. 16. Testing Strategies for AI Applications. End Matter. Answer Keys for Chapter Questions.