Cognitive Cloud Systems: The Convergence of AI, LLMs, and Next-Generation Service Architectures
Keywords:
Cognitive Cloud Systems, Artificial Intelligence in Cloud, Large Language Models (LLMs), Wissira Press, Wissira Press Academic Books, Books by WissiraSynopsis
Cloud computing has reached a point of architectural maturity. What began as a shift toward elasticity and infrastructure abstraction through containers, microservices, service meshes, and serverless platforms has now become the default operating model for modern software systems. Yet, even as cloud-native practices have stabilized, a deeper transformation is unfolding. Artificial intelligence, machine learning, and large language models are no longer peripheral enhancements layered on top of applications; they are reshaping the very foundations of how systems are designed, operated, and evolved.
Cognitive Cloud Systems: The Convergence of AI, LLMs, and Next-Generation Service Architectures is written at this inflection point. The central premise of this book is simple but far-reaching: intelligence is becoming a core architectural property of the cloud. Modern systems are not only scalable and resilient, but they are also increasingly aware, predictive, and capable of limited, policy-bound autonomy. They observe their own behavior, anticipate demand and failure, and assist humans in making faster, more informed decisions.
The book is designed for a broad but connected audience. Software and platform engineers will find guidance on embedding inference into services, designing semantic and AI-aware APIs, and operating data and vector infrastructure at scale. Reliability and operations teams will learn how predictive signals enhance autoscaling, scheduling, and incident management. Security and compliance professionals will explore patterns for protecting AI pathways, enforcing zero-trust principles, and governing models and prompts as production assets. Technology and product leaders will gain a common language for evaluating cost, risk, and return, and for planning incremental, outcome-driven adoption of cognitive capabilities.
Our hope is that the chapters ahead help you design and operate cloud systems that are not only scalable and reliable, but thoughtful systems that learn, assist, and evolve responsibly. Progress will come incrementally: one well-instrumented service, one governed model, and one carefully designed feedback loop at a time.
Chapters
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Chapter 1: Cloud-Native + AI: The New Foundation
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Chapter 2: Microservices to Micromodels
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Chapter 3: Serverless & FaaS with Intelligent Runtimes
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Chapter 4: Data, Features, and Vector Infrastructure
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Chapter 5: Orchestration, Autoscaling, and Placement
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Chapter 6: Reliability, Self-Healing, and AIOps
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Chapter 7: Observability, Governance, and AI-Driven Ops
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Chapter 8: Security, Privacy, and Zero-Trust for AI Workloads
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Chapter 9: Performance, Cost, and Sustainability Engineering
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Chapter 10: Patterns, Case Studies, and Migration Roadmaps
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