Sireesha Muppala is a visionary technology leader with over 25 years of experience driving digital transformation and unlocking the potential of cutting-edge innovations. As a Senior Solutions Architecture Leader at Amazon Web Services (AWS), Sireesha spearheads efforts to empower businesses with the transformative power of generative AI and other emerging technologies. She currently leads a team of dedicated technologists at AWS, focused on partnering with leading organizations in the automotive and manufacturing sectors to unlock the full potential of generative AI and other cutting-edge cloud-based technologies.
Sireesha's remarkable career has been fueled by her passion for technology and her keen ability to identify disruptive trends. Sireesha received her Ph.D. in computer science from the University of Colorado, Colorado Springs. Prior to joining AWS, she held leadership roles at organizations of all sizes across diverse industries, including Oracle, Blackhawk Network, and Primer AI, where she was instrumental in developing and implementing groundbreaking solutions that addressed complex business challenges.
Sireesha's technical expertise is complemented by her strategic vision and collaborative approach, which have earned her a reputation as a trusted advisor to C-suite executives and industry leaders. An accomplished author and sought-after speaker, Sireesha has authored numerous research papers, white papers, and the acclaimed book "Amazon SageMaker Best Practices " . She frequently shares her insights on the transformative power of emerging technologies at industry conferences and events, inspiring audiences with her deep understanding of the evolving technology landscape and its impact on the future of business.
Beyond her professional achievements, Sireesha is a passionate advocate for STEM education and empowering underrepresented communities in the technology sector. She is a co-founder of the Denver chapter of women in big data, where she leverages her expertise to create meaningful change and inspire the next generation of innovators.
With her unwavering commitment to innovation and her ability to translate complex technical concepts into practical business solutions, Sireesha Muppala is poised to continue leading the charge in the ever-evolving world of emerging technologies at AWS, driving transformative change and unlocking new frontiers of growth for organizations.
Randy DeFauw is a technologist at heart. While majoring in electrical engineering at the University of Michigan, he took part in an autonomous vehicle research program. That started his journey into signal processing and what we now call computer vision. Randy applied his image processing expertise on a major defense program in San Diego, before deciding to pursue more customer-facing roles.
Randy worked in consulting, product management, and architecture for several years. In the course of that journey, he worked at a startup that was making distributed storage technology for big data systems. That role reignited his passion for working with data, and also gave him a solid grounding in modern distributed system design. At this point he picked up an MBA to give him the business grounding to go along with his two electrical engineering degrees. He then led a private cloud architecture team on a multi-year project. The intersection of data and cloud was the ideal spot for him, as the cloud unlocked so much potential for data and ML projects.
Randy then moved to AWS, where he’s helped customers architect a variety of cloud applications. He focuses on the data ecosystem, from big data to machine learning and now generative AI. Randy likes to focus on the unsolved problem areas, like LLM interpretation and solving operations research problems through reinforcement learning. He publishes frequently in various channels, and often authors sample code published on GitHub. He’s a trusted technical advisor to customers and still a hands- on practitioner.
Since his days as a teaching assistant in university, Randy never lost his enthusiasm for helping others learn. He works closely with early career colleagues, mentors at the high school and college level, and is an advisor to a Colorado K-12 education program on STEM initiatives.
Sina Sojoodi is a technology executive and innovator with nearly two decades of experience driving digital transformation across global enterprises. As the co-founder and CTO of 8090, he leads the development of AI-powered software solutions. These solutions target improving business efficiency by 80% and reducing costs by 90%. His entrepreneurial achievements include founding and being the founding team member of multiple ventures, leading to two M&A exits and an IPO. He holds patents in push notification activation, dynamic rendering for software applications, and image annotation systems.
At (AWS), Sina advised Fortune 500 companies, government agencies, and leading technology startups. He implemented AI solutions across regulated and unregulated environments. His work spans speech recognition, conversational AI, MLOps, and software transformation initiatives. He serves as a board and executive advisor to B2B software companies and startups, guiding their integration of generative AI into core products and services.
Prior to AWS, Sina served as a Global Field CTO for application and data architecture at VMware, advising C-level executives on cloud and application modernization strategies. He joined VMware through the Pivotal Software acquisition. At Pivotal, he served in multiple roles including Field CTO and Director of Product and Engineering. He led R&D post-M&A strategies by integrating his team at Xtreme Labs with the Pivotal Cloud Foundry R&D team and expanding Pivotal’s platform with innovative mobile-backend-as-service capabilities.
Sina holds a bachelor's degree from the University of Waterloo, where he studied electrical engineering and psychology. This combination shapes his approach to building human-centric technology solutions. His understanding of technical implementation and business strategy makes him a valued advisor for organizations navigating AI adoption and digital transformation.
Through his work at 8090, Sina continues to shape the future of enterprise technology. He focuses on helping organizations achieve new levels of efficiency and cost optimization. His practical approach to AI implementation guides organizations in their transformation and modernization efforts.
Generative AI is transforming every industry, with applications ranging from creative content generation, simple chatbots, to entirely new ways of engaging with consumers. But there is as much uncertainty as buzz—understanding how to use this technology securely and responsibly, and recognizing what the pitfalls are.
In this book, we will put together a complete picture of generative AI development on modern cloud platforms, covering all stages of building and operating a production-grade solution with consideration for performance, security, governance, and responsibility. Conceptual discussions will be accompanied by functional examples, using working code on Amazon Web Services (AWS) cloud to demonstrate key concepts. We will explore the full lifecycle, from initial model selection and fine-tuning to production deployment, monitoring, and ongoing operation. Key aspects include prompt engineering, data integration techniques, observability, the shared responsibility model, and the full solution lifecycle from design to operation. Additionally, we will discuss recommendations for prioritizing a generative AI roadmap for organizations and emerging trends in the field.
As readers progress, they will gain insights into the future trends of AI and witness its transformative impact across various industries through case studies. By the end of the book, the readers will have a solid understanding of the features of foundational models and their collaboration with cloud computing, enabling them to create innovative, efficient, and ethical AI solutions in diverse cloud-based applications.
WHAT YOU WILL LEARN
* Basics of cloud computing and evolution of generative AI.
* Complete solution stack for generative AI to address security and performance concerns.
* Prompt engineering for improving performance and security concerns.
* Framework for the responsible use of AI to judge risks and put safeguards in place.
* Advanced fine-tuning smaller models to get effective performance at lower costs.
* Integration with data and tools to expand the power of generative AI and handle complex workflows and access new information.
WHO THIS BOOK IS FOR
This book is for cloud architects, engineers, data analysts, and AI professionals. Readers should possess foundational cloud and ML knowledge; generative AI expertise is not required.
