Empowering the Public Sector with Generative AI From Strategy and Design to Real-World Applications

This is your guide book to Generative AI (GenAI) and its application in addressing real-world challenges within the public sector. The book addresses a range of topics from GenAI concepts and strategy to public sector use cases, architecture patterns, and implementation best practices. With a general background in technology and the public sector, you will be able to understand the concepts in this book.

The book will help you develop a deeper understanding of GenAI and learn how GenAI differs from traditional AI. You will explore best practices such as prompt engineering, and fine-tuning, and architectural patterns such as Retrieval Augmented Generation (RAG). And you will discover specific nuances, considerations, and strategies for implementation in a public sector organization. 

You will understand how to apply these concepts in a public sector setting and address industry-specific challenges and problems by studying a variety of use cases included in the book in the areas of content generation, chatbots, summarization, and program management.

What You Will Learn

• GenAI concepts and how GenAI differs from traditional AI/ML 
• Prompt engineering, fine-tuning, RAG, and customizing foundation models
• Strategy, methodologies, and frameworks for the public sector
• Public sector use cases in the areas of content generation, summarization, and chatbots, plus program management, analytics, business intelligence, and reporting
• Architecture and design patterns 
• Implementation, operations, and maintenance of GenAI applications

Who This Book Is For

Technology and business leaders in the public sector who are new to AI/ML and are keen on exploring and harnessing the potential of Generative AI in their respective organizations.

Chapter 1: Introduction to Generative AI.- Chapter 2: Generative AI in the Public Sector.- Chapter 3: Gen AI Strategy: A Blueprint for Successful Adoption.- Chapter 4: Building a Generative AI Application.-  Chapter 5: Content Generation.- Chapter 6: Chatbots and Enterprise Search.- Chapter 7: Summarization.- Chapter 8: Program Management, Business Intelligence, and Reporting.- Chapter 9: Implementation Considerations.- Chapter 10: Conclusion.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- Appendix E.

 Sanjeev Pulapaka is Principal Solutions Architect at Amazon Web Services (AWS). He leads the development of AI/ML and Generative AI solutions for the US Federal Civilian team. Sanjeev has extensive experience in leading, architecting, and implementing high-impact technology solutions that address diverse business needs in multiple sectors (including commercial, federal, and state and local governments).  He has published numerous blogs and white papers on AI/ML and is an active speaker and panelist at various industry conferences, including AWS Public Sector Summit and AWS re:Invent. Sanjeev has an undergraduate degree in engineering from the Indian Institute of Technology and an MBA degree from the University of Notre Dame.

Srinath Godavarthi has over 20 years of experience serving public sector customers and he held leadership positions with global technology and consulting companies ,including Amazon and Accenture. In his previous roles, Srinath led cloud strategy, architecture, and digital transformation efforts for a number of federal, state, and local agencies. Srinath specializes in AI/ML technologies and has published over a dozen white papers and blogs on various topics (including AI, ML, and Healthcare). He has been a speaker at various industry conferences, including the AWS Public Sector Summit, AWS re:Invent, and the American Public Human Services Association. He holds a master’s degree in computer science from Temple University and completed a Chief Technology Officer program from the University of California, Berkeley.

Sherry Ding is an artificial intelligence and machine learning (AI/ML) technologist and evangelist with 20 years of experience in AI/ML research and applications. She currently works at Amazon Web Services as an AI/ML Specialist Solutions Architect, serving public sector customers on their AI/ML related business challenges,  and guiding them to build highly reliable and scalable AI/ML applications on the cloud. Sherry holds a PhD in computer science from Korea University. She has  authored more than 30 publications (including journal articles, book chapters, white papers, conference proceedings, and blogs) on different topics related to AI/ML. She is an active public speaker who has presented at various academia and industry conferences such as IEEE conferences, AWS re:Invent, and AWS Summits.