- Preface
- A Primer on the Nine-Layer Stack
- PART VII — LAYER 6: TRAINING & MLOPS
- Chapter 17 — Training Platforms and ML Lifecycle
- Chapter 18 — Fine-Tuning Specifics
- PART VIII — LAYER 7: INFERENCE & SERVING
- Chapter 19 — Inference Engines
- Chapter 20 — Serving Infrastructure
- PART IX — LAYER 8: OBSERVABILITY
- Chapter 21 — Production LLM Observability
- Chapter 22 — Evaluation Infrastructure and Prompt Management
- PART X — LAYER 9: GOVERNANCE
- Chapter 23 — Prompt-Injection Defences and Content Moderation
- Chapter 24 — AI Security Testing and Compliance Governance
- Chapter 25 — AI Red-Teaming Platforms and Supply Chain Security
- PART XI — SYNTHESIS AND TRAJECTORY
- Chapter 26 — The Composition: How the Nine Layers Become Production Systems
- Chapter 27 — The 2027 Trajectory: Where the Stack Is Heading
- Chapter 28 — The Architect's Posture
- PART XII — ENTERPRISE AI IN PRACTICE
- Chapter 29 — AI Economics, ROI, and Business Value
- Chapter 30 — Enterprise AI Platforms
- Chapter 31 — Enterprise AI Operating Model
- Chapter 32 — Enterprise AI Maturity Model
- Chapter 33 — Industry Reference Architectures
- Executive Summaries and CIO Takeaways
- Appendix B — Architecture Review Checklists
- BACK MATTER
- Glossary of Recurring Terms
- Index of the Hundred Tools
- Selected Bibliography and Primary Sources
- About the Author
ENTERPRISE AI ARCHITECTURE AND THE MODERN AI STACK
VOLUME II — RUNNING THE STACK
This book is 100% completeLast updated on 2026-07-20
Designing AI is only half the journey. Learn how to deploy, secure, govern, monitor, and scale Enterprise AI systems for production with practical architectures and proven operational strategies.
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About
About the Book
Enterprise AI Architecture and the Modern AI Stack – Volume II builds upon the architectural foundations established in Volume I and takes readers into the practical realities of running AI at enterprise scale.
Designing an AI solution is only the beginning. Organizations must also deploy, secure, monitor, govern, and continuously improve AI systems while meeting enterprise requirements for reliability, compliance, scalability, and cost efficiency. This volume explores the operational side of Enterprise AI and provides a practical blueprint for transforming AI prototypes into production-ready platforms.
Inside this book, you'll learn how to:
- Deploy Enterprise AI applications across cloud and hybrid environments
- Build scalable AI platforms using modern infrastructure and orchestration
- Secure AI systems with identity, API security, zero-trust principles, and data protection
- Implement governance, compliance, responsible AI, and risk management
- Monitor AI systems through observability, logging, tracing, and performance metrics
- Establish AI operations (AIOps and LLMOps) for continuous delivery and lifecycle management
- Design resilient enterprise architectures that support high availability, scalability, and disaster recovery
- Optimize AI performance, infrastructure costs, and operational efficiency
- Apply enterprise architecture best practices through reference architectures, implementation patterns, and real-world scenarios
Written from a vendor-neutral perspective, this book focuses on architectural principles that remain valuable regardless of the underlying technology stack. Whether your organization uses OpenAI, Anthropic, Google, Azure, AWS, SAP, open-source models, or hybrid AI platforms, the concepts presented here provide a durable foundation for building production-ready Enterprise AI systems.
This book is intended for Enterprise Architects, AI Architects, Solution Architects, Platform Engineers, AI Engineers, Cloud Architects, Technology Leaders, and software professionals responsible for delivering secure, scalable, and governed AI solutions.
Together, Volume I: Designing the Stack and Volume II: Running the Stack provide a comprehensive guide to Enterprise AI Architecture—from foundational design principles to operating intelligent systems in production.
Author
About the Author
Padmanabham Venkiteela
Padmanabham Venkiteela is an Enterprise Integration Architect, Enterprise AI Architect, researcher, author, and international speaker with nearly two decades of experience designing and modernizing enterprise technology platforms for global organizations.
His expertise spans Enterprise AI Architecture, Agentic AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, API Management, enterprise integration, cloud platforms, SAP Business Technology Platform (SAP BTP), Google Cloud, and digital transformation. Throughout his career, he has led complex modernization initiatives, helping organizations build secure, scalable, and resilient enterprise systems.
Padmanabham is the author of the Enterprise AI Architecture and the Modern AI Stack series, which provides a practical, vendor-neutral framework for designing, deploying, operating, and governing enterprise AI solutions. His work bridges architectural theory with real-world implementation, enabling technology leaders and engineering teams to move AI from experimentation to production.
In addition to authoring books, he publishes technical research, speaks at international conferences, serves as a reviewer and judge for technology conferences and innovation programs, and actively contributes to the global technology community.
Learn more about his work, research, publications, and upcoming books at https://padmanabhamvenkiteela.com.
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