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

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

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