Del i serien 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

939 kr

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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.

Produktinformation

Utgivningsdatum2026-07-06
Mått156 x 234 x undefined mm
Vikt580 g
FormatHäftad
SpråkEngelska
SerieWhat Every Engineer Should Know
Antal sidor304
FörlagTaylor & Francis Ltd
ISBN9781032829852

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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.

Part I Foundations and Platforms: Automation and Data Quality at ScaleChapter 1 Fundamental Concepts in AIChapter 2 Big Data and Artificial Intelligence SystemsChapter 3 Architecting Big Data PipelinesChapter 4 Big Data Frameworks and Data Cleaning StrategiesChapter 5 Building Automated Pipelines for Data CleaningPart II Optimization and SearchChapter 6 Swarm IntelligenceChapter 7 Genetic ProgrammingPart III Learning SystemsChapter 8 Foundations on Machine Learning and Artificial LearningChapter 9 Reinforcement LearningChapter 10 Deep Reinforcement LearningChapter 11 Natural Language ModelingChapter 12 Transformer Architecture and Evolution of LLMsPart IV Systems in the Real WorldChapter 13 Architecting Distributed AI Systems Using Design PatternsChapter 14 Securing AI SystemsChapter 15 AI System Safety in PracticeChapter 16 Testing Strategies for AI ApplicationsAnswer Keys for Chapter Questions