1st Edition
What Every Engineer Should Know About Artificial Intelligence and Big Data
Part I Foundations and Platforms: Automation and Data Quality at Scale
Chapter 1 Fundamental Concepts in AI
Chapter 2 Big Data and Artificial Intelligence Systems
Chapter 3 Architecting Big Data Pipelines
Chapter 4 Big Data Frameworks and Data Cleaning Strategies
Chapter 5 Building Automated Pipelines for Data Cleaning
Part II Optimization and Search
Chapter 6 Swarm Intelligence
Chapter 7 Genetic Programming
Part III Learning Systems
Chapter 8 Foundations on Machine Learning and Artificial Learning
Chapter 9 Reinforcement Learning
Chapter 10 Deep Reinforcement Learning
Chapter 11 Natural Language Modeling
Chapter 12 Transformer Architecture and Evolution of LLMs
Part IV Systems in the Real World
Chapter 13 Architecting Distributed AI Systems Using Design Patterns
Chapter 14 Securing AI Systems
Chapter 15 AI System Safety in Practice
Chapter 16 Testing Strategies for AI Applications
Answer Keys for Chapter Questions
Biography
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.
