What Every Engineer Should Know About Artificial Intelligence and Big Data
Language: English
Published by Taylor and Francis Ltd, GB, 2026
- Softcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
Condition: New
£ 66.45
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Add to basketItem description from seller
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 toward 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. In addition to focusing on theory, this 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 solutionsDiscusses the use of design patterns for architecting AI that are safe, secure, and testableCovers an array of concepts, including deep big data analytics, natural language processing, transformer architecture, and evolution of ChatGPT, swarm intelligence, and genetic programmingInformed by the authors' many years of teaching ML and 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. …
Seller Inventory # LU-9781032829852
- Title
- What Every Engineer Should Know About Artificial Intelligence and Big Data
- Author
- Satish Mahadevan Srinivasan, Raghvinder S. Sangwan
- Publisher
- Taylor and Francis Ltd, GB
- Publication year
- 2026
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1032829850
- ISBN 13
- 9781032829852
- Item weight
- 580 grams
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 toward 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. In addition to focusing on theory, this 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 and 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.
"Synopsis" may belong to another edition of this title.
About the Author
Satish Mahadevan Srinivasan is an Associate Professor of Information Science at Pennsylvania State University, Great Valley. He teaches courses related to database design, data mining, data collection and cleaning, data visualization, computer, network and web securities, network analytics and business process management.
Raghvinder S. Sangwan 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.
"About the title" may belong to another edition of this title.
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