Sairohith Thummarakoti – författare

The healthcare industry is undergoing a transformative shift as artificial intelligence moves from isolated predictive models to autonomous, goal-driven agents capable of reasoning, planning, and adapting in real time.Agentic AI for Healthcare: Building Scalable Models for Production is the first comprehensive technical guide that focuses specifically on designing and deploying agentic AI systems within complex clinical and operational healthcare environments. Unlike traditional AI systems that passively infer outcomes, agentic AI involves systems that actively make decisions, coordinate with other agents, and take initiative toward long-term goals, making them ideal for dynamic and high-stakes healthcare settings. What sets this book apart is its deep technical focus tailored for computer scientists, architects, and AI engineers who are building real-world healthcare solutions. Each chapter bridges theoretical foundations with production-ready implementations using cloud-native technologies, distributed architectures, knowledge representation models, and secure deployment pipelines. With detailed case studies in oncology, vaccine management, and hospital operations, the book demonstrates how agentic AI can improve care delivery, reduce cognitive burden on clinicians, and automate routine workflows - all while meeting regulatory standards.As healthcare increasingly adopts AI-first strategies, this book fills a critical need by offering a structured, engineering-centered approach to building scalable, intelligent, and trustworthy healthcare systems.

The healthcare industry is undergoing a transformative shift as artificial intelligence moves from isolated predictive models to autonomous, goal-driven agents capable of reasoning, planning, and adapting in real time.Agentic AI for Healthcare: Building Scalable Models for Production is the first comprehensive technical guide that focuses specifically on designing and deploying agentic AI systems within complex clinical and operational healthcare environments. Unlike traditional AI systems that passively infer outcomes, agentic AI involves systems that actively make decisions, coordinate with other agents, and take initiative toward long-term goals, making them ideal for dynamic and high-stakes healthcare settings. What sets this book apart is its deep technical focus tailored for computer scientists, architects, and AI engineers who are building real-world healthcare solutions. Each chapter bridges theoretical foundations with production-ready implementations using cloud-native technologies, distributed architectures, knowledge representation models, and secure deployment pipelines. With detailed case studies in oncology, vaccine management, and hospital operations, the book demonstrates how agentic AI can improve care delivery, reduce cognitive burden on clinicians, and automate routine workflows - all while meeting regulatory standards.As healthcare increasingly adopts AI-first strategies, this book fills a critical need by offering a structured, engineering-centered approach to building scalable, intelligent, and trustworthy healthcare systems.

This book offers a comprehensive, practice-driven guide to designing and managing robust AI cloud infrastructure systems for real-world applications.As enterprises continue to adopt AI to enhance automation, decision-making, and customer engagement, there is a growing demand for cloud-native architectures that can scale with increasing data volumes, support model training, ensure operational efficiency, and meet stringent security and governance requirements. This book addresses that demand by equipping readers with the foundational knowledge and advanced strategies needed to build, deploy, and maintain AI systems on modern cloud platforms. What makes this book unique is its end-to-end perspective, which goes beyond traditional AI model development. It covers key pillars such as hybrid and multi-cloud strategies, container orchestration, serverless computing, edge AI deployment, AI governance, cost optimization, and sustainable computing, all framed around the AI model lifecycle. Readers will gain practical insights through architectural diagrams, platform comparisons (AWS, Azure, GCP), and use cases across healthcare, finance, and manufacturing. It also explores the integration of AutoML, MLOps, quantum computing, and green AI within cloud ecosystems. This book fills a critical gap by merging cloud infrastructure engineering with AI-specific challenges, offering a rare blend of systems thinking and AI expertise.Targeted toward architects, data scientists, DevOps engineers, cloud professionals, and graduate students, it serves as both a reference guide and a strategic roadmap for building future-ready AI systems in the cloud.

This book offers a comprehensive, practice-driven guide to designing and managing robust AI cloud infrastructure systems for real-world applications.As enterprises continue to adopt AI to enhance automation, decision-making, and customer engagement, there is a growing demand for cloud-native architectures that can scale with increasing data volumes, support model training, ensure operational efficiency, and meet stringent security and governance requirements. This book addresses that demand by equipping readers with the foundational knowledge and advanced strategies needed to build, deploy, and maintain AI systems on modern cloud platforms. What makes this book unique is its end-to-end perspective, which goes beyond traditional AI model development. It covers key pillars such as hybrid and multi-cloud strategies, container orchestration, serverless computing, edge AI deployment, AI governance, cost optimization, and sustainable computing, all framed around the AI model lifecycle. Readers will gain practical insights through architectural diagrams, platform comparisons (AWS, Azure, GCP), and use cases across healthcare, finance, and manufacturing. It also explores the integration of AutoML, MLOps, quantum computing, and green AI within cloud ecosystems. This book fills a critical gap by merging cloud infrastructure engineering with AI-specific challenges, offering a rare blend of systems thinking and AI expertise.Targeted toward architects, data scientists, DevOps engineers, cloud professionals, and graduate students, it serves as both a reference guide and a strategic roadmap for building future-ready AI systems in the cloud.

Modern enterprises must address three concurrent challenges: rapid technological advancement, increasing regulatory requirements, and heightened customer expectations. In this context, the concept of cloud has evolved from a destination to an operating model. Pega Cloud 3 exemplifies this transformation. It provides a cloud-native execution environment, including pods for strict isolation, externalized stateful services for enhanced performance, Kubernetes for automated recovery and continuous delivery, and integrated low-code decisioning with governance. Pega Agentic AI on Cloud 3 synthesizes these components into a structured methodology, enabling agentic artificial intelligence (AI) that operates with evidence-based planning and defined safeguards. Key features of the book include:

Priority is given to actionable decisions over extensive definitions. Where terminology could become ambiguous, a default is provided along with justification for any deviations, facilitating team progress Evidence is treated as the primary deliverable. Automated pipelines are emphasized over static screenshots, and audits are structured as exportable data rather than informal searches Rollbacks are occasionally necessary. Rollback procedures are designed, rehearsed, and standardized to maintain operational confidence

The book is a practical guide focusing on actionable methodologies rather than theoretical concepts. Each architectural pattern supports incremental adoption, with all controls implemented programmatically, and every change is subject to audit and review. The book features a continuous case study, Northstar Enterprises, a diversified organization that adopted Cloud 3 to accelerate delivery, efficiently demonstrate compliance, and align scaling with measurable customer outcomes.

Modern enterprises must address three concurrent challenges: rapid technological advancement, increasing regulatory requirements, and heightened customer expectations. In this context, the concept of cloud has evolved from a destination to an operating model. Pega Cloud 3 exemplifies this transformation. It provides a cloud-native execution environment, including pods for strict isolation, externalized stateful services for enhanced performance, Kubernetes for automated recovery and continuous delivery, and integrated low-code decisioning with governance. Pega Agentic AI on Cloud 3 synthesizes these components into a structured methodology, enabling agentic artificial intelligence (AI) that operates with evidence-based planning and defined safeguards. Key features of the book include:

Priority is given to actionable decisions over extensive definitions. Where terminology could become ambiguous, a default is provided along with justification for any deviations, facilitating team progress Evidence is treated as the primary deliverable. Automated pipelines are emphasized over static screenshots, and audits are structured as exportable data rather than informal searches Rollbacks are occasionally necessary. Rollback procedures are designed, rehearsed, and standardized to maintain operational confidence

The book is a practical guide focusing on actionable methodologies rather than theoretical concepts. Each architectural pattern supports incremental adoption, with all controls implemented programmatically, and every change is subject to audit and review. The book features a continuous case study, Northstar Enterprises, a diversified organization that adopted Cloud 3 to accelerate delivery, efficiently demonstrate compliance, and align scaling with measurable customer outcomes.

Generative AI in Software Development: A Practical Guide for Engineering Leaders explores how large language models and generative tools are fundamentally changing the way software is created, tested, and maintained. This isn't a theoretical or academic deep dive; it's a practical, grounded guide for developers, product teams, and tech leaders who want to understand how generative AI can be embedded into real-world software workflows. From writing and debugging code to generating UI components, managing APIs, and automating deployment tasks, this book shows how AI is becoming a true co-developer.What sets this book apart is its balance: it's not just for hardcore engineers nor is it a high-level overview filled with buzzwords. Instead, it sits at the intersection technical enough to be useful but accessible enough for product managers and decision-makers. The industry is at an inflection point where AI-assisted development is no longer optional; it's become essential. This book fills the gap for those who want to move beyond the hype and see exactly how generative AI can speed up development, improve code quality, and change the economics of software delivery.

Generative AI in Software Development: A Practical Guide for Engineering Leaders explores how large language models and generative tools are fundamentally changing the way software is created, tested, and maintained. This isn't a theoretical or academic deep dive; it's a practical, grounded guide for developers, product teams, and tech leaders who want to understand how generative AI can be embedded into real-world software workflows. From writing and debugging code to generating UI components, managing APIs, and automating deployment tasks, this book shows how AI is becoming a true co-developer.What sets this book apart is its balance: it's not just for hardcore engineers nor is it a high-level overview filled with buzzwords. Instead, it sits at the intersection technical enough to be useful but accessible enough for product managers and decision-makers. The industry is at an inflection point where AI-assisted development is no longer optional; it's become essential. This book fills the gap for those who want to move beyond the hype and see exactly how generative AI can speed up development, improve code quality, and change the economics of software delivery.

The healthcare industry is undergoing a transformative shift as artificial intelligence moves from isolated predictive models to autonomous, goal-driven agents capable of reasoning, planning, and adapting in real time.Agentic AI for Healthcare: Building Scalable Models for Production is the first comprehensive technical guide that focuses specifically on designing and deploying agentic AI systems within complex clinical and operational healthcare environments. Unlike traditional AI systems that passively infer outcomes, agentic AI involves systems that actively make decisions, coordinate with other agents, and take initiative toward long-term goals, making them ideal for dynamic and high-stakes healthcare settings. What sets this book apart is its deep technical focus tailored for computer scientists, architects, and AI engineers who are building real-world healthcare solutions. Each chapter bridges theoretical foundations with production-ready implementations using cloud-native technologies, distributed architectures, knowledge representation models, and secure deployment pipelines. With detailed case studies in oncology, vaccine management, and hospital operations, the book demonstrates how agentic AI can improve care delivery, reduce cognitive burden on clinicians, and automate routine workflows – all while meeting regulatory standards.As healthcare increasingly adopts AI-first strategies, this book fills a critical need by offering a structured, engineering-centered approach to building scalable, intelligent, and trustworthy healthcare systems.

The healthcare industry is undergoing a transformative shift as artificial intelligence moves from isolated predictive models to autonomous, goal-driven agents capable of reasoning, planning, and adapting in real time.Agentic AI for Healthcare: Building Scalable Models for Production is the first comprehensive technical guide that focuses specifically on designing and deploying agentic AI systems within complex clinical and operational healthcare environments. Unlike traditional AI systems that passively infer outcomes, agentic AI involves systems that actively make decisions, coordinate with other agents, and take initiative toward long-term goals, making them ideal for dynamic and high-stakes healthcare settings. What sets this book apart is its deep technical focus tailored for computer scientists, architects, and AI engineers who are building real-world healthcare solutions. Each chapter bridges theoretical foundations with production-ready implementations using cloud-native technologies, distributed architectures, knowledge representation models, and secure deployment pipelines. With detailed case studies in oncology, vaccine management, and hospital operations, the book demonstrates how agentic AI can improve care delivery, reduce cognitive burden on clinicians, and automate routine workflows – all while meeting regulatory standards.As healthcare increasingly adopts AI-first strategies, this book fills a critical need by offering a structured, engineering-centered approach to building scalable, intelligent, and trustworthy healthcare systems.

This book offers a comprehensive, practice-driven guide to designing and managing robust AI cloud infrastructure systems for real-world applications.As enterprises continue to adopt AI to enhance automation, decision-making, and customer engagement, there is a growing demand for cloud-native architectures that can scale with increasing data volumes, support model training, ensure operational efficiency, and meet stringent security and governance requirements. This book addresses that demand by equipping readers with the foundational knowledge and advanced strategies needed to build, deploy, and maintain AI systems on modern cloud platforms. What makes this book unique is its end-to-end perspective, which goes beyond traditional AI model development. It covers key pillars such as hybrid and multi-cloud strategies, container orchestration, serverless computing, edge AI deployment, AI governance, cost optimization, and sustainable computing, all framed around the AI model lifecycle. Readers will gain practical insights through architectural diagrams, platform comparisons (AWS, Azure, GCP), and use cases across healthcare, finance, and manufacturing. It also explores the integration of AutoML, MLOps, quantum computing, and green AI within cloud ecosystems. This book fills a critical gap by merging cloud infrastructure engineering with AI-specific challenges, offering a rare blend of systems thinking and AI expertise.Targeted toward architects, data scientists, DevOps engineers, cloud professionals, and graduate students, it serves as both a reference guide and a strategic roadmap for building future-ready AI systems in the cloud.

This book offers a comprehensive, practice-driven guide to designing and managing robust AI cloud infrastructure systems for real-world applications.As enterprises continue to adopt AI to enhance automation, decision-making, and customer engagement, there is a growing demand for cloud-native architectures that can scale with increasing data volumes, support model training, ensure operational efficiency, and meet stringent security and governance requirements. This book addresses that demand by equipping readers with the foundational knowledge and advanced strategies needed to build, deploy, and maintain AI systems on modern cloud platforms. What makes this book unique is its end-to-end perspective, which goes beyond traditional AI model development. It covers key pillars such as hybrid and multi-cloud strategies, container orchestration, serverless computing, edge AI deployment, AI governance, cost optimization, and sustainable computing, all framed around the AI model lifecycle. Readers will gain practical insights through architectural diagrams, platform comparisons (AWS, Azure, GCP), and use cases across healthcare, finance, and manufacturing. It also explores the integration of AutoML, MLOps, quantum computing, and green AI within cloud ecosystems. This book fills a critical gap by merging cloud infrastructure engineering with AI-specific challenges, offering a rare blend of systems thinking and AI expertise.Targeted toward architects, data scientists, DevOps engineers, cloud professionals, and graduate students, it serves as both a reference guide and a strategic roadmap for building future-ready AI systems in the cloud.

Modern enterprises must address three concurrent challenges: rapid technological advancement, increasing regulatory requirements, and heightened customer expectations. In this context, the concept of cloud has evolved from a destination to an operating model. Pega Cloud 3 exemplifies this transformation. It provides a cloud-native execution environment, including pods for strict isolation, externalized stateful services for enhanced performance, Kubernetes for automated recovery and continuous delivery, and integrated low-code decisioning with governance. Pega Agentic AI on Cloud 3 synthesizes these components into a structured methodology, enabling agentic artificial intelligence (AI) that operates with evidence-based planning and defined safeguards. Key features of the book include:Priority is given to actionable decisions over extensive definitions. Where terminology could become ambiguous, a default is provided along with justification for any deviations, facilitating team progressEvidence is treated as the primary deliverable. Automated pipelines are emphasized over static screenshots, and audits are structured as exportable data rather than informal searchesRollbacks are occasionally necessary. Rollback procedures are designed, rehearsed, and standardized to maintain operational confidenceThe book is a practical guide focusing on actionable methodologies rather than theoretical concepts. Each architectural pattern supports incremental adoption, with all controls implemented programmatically, and every change is subject to audit and review. The book features a continuous case study, Northstar Enterprises, a diversified organization that adopted Cloud 3 to accelerate delivery, efficiently demonstrate compliance, and align scaling with measurable customer outcomes.

Modern enterprises must address three concurrent challenges: rapid technological advancement, increasing regulatory requirements, and heightened customer expectations. In this context, the concept of cloud has evolved from a destination to an operating model. Pega Cloud 3 exemplifies this transformation. It provides a cloud-native execution environment, including pods for strict isolation, externalized stateful services for enhanced performance, Kubernetes for automated recovery and continuous delivery, and integrated low-code decisioning with governance. Pega Agentic AI on Cloud 3 synthesizes these components into a structured methodology, enabling agentic artificial intelligence (AI) that operates with evidence-based planning and defined safeguards. Key features of the book include:Priority is given to actionable decisions over extensive definitions. Where terminology could become ambiguous, a default is provided along with justification for any deviations, facilitating team progressEvidence is treated as the primary deliverable. Automated pipelines are emphasized over static screenshots, and audits are structured as exportable data rather than informal searchesRollbacks are occasionally necessary. Rollback procedures are designed, rehearsed, and standardized to maintain operational confidenceThe book is a practical guide focusing on actionable methodologies rather than theoretical concepts. Each architectural pattern supports incremental adoption, with all controls implemented programmatically, and every change is subject to audit and review. The book features a continuous case study, Northstar Enterprises, a diversified organization that adopted Cloud 3 to accelerate delivery, efficiently demonstrate compliance, and align scaling with measurable customer outcomes.

Generative AI in Software Development: A Practical Guide for Engineering Leaders explores how large language models and generative tools are fundamentally changing the way software is created, tested, and maintained. This isn’t a theoretical or academic deep dive; it’s a practical, grounded guide for developers, product teams, and tech leaders who want to understand how generative AI can be embedded into real-world software workflows. From writing and debugging code to generating UI components, managing APIs, and automating deployment tasks, this book shows how AI is becoming a true co-developer.What sets this book apart is its balance: it’s not just for hardcore engineers nor is it a high-level overview filled with buzzwords. Instead, it sits at the intersection technical enough to be useful but accessible enough for product managers and decision-makers. The industry is at an inflection point where AI-assisted development is no longer optional; it’s become essential. This book fills the gap for those who want to move beyond the hype and see exactly how generative AI can speed up development, improve code quality, and change the economics of software delivery.