Software delivery is undergoing a fundamental transformation. As systems grow more complex and deployment cycles accelerate, traditional DevOps practices are reaching their limits. The next evolution—Cognitive DevOps—integrates artificial intelligence, machine learning, and large language models (LLMs) to create intelligent, predictive, and self-optimizing software delivery pipelines. This book provides a comprehensive guide to building AI-augmented DevOps systems, enabling organizations to move beyond automation toward autonomous decision-making in software engineering. Beginning with the evolution of software engineering and DevOps, the book introduces the concept of Cognitive DevOps and explores how AI can enhance every stage of the CI/CD lifecycle. It demonstrates how LLMs are transforming development workflows—from code understanding and generation to automated code reviews and intelligent feedback systems. Key highlights include: Understanding the shift from traditional DevOps to AI-driven Cognitive DevOps Leveraging large language models (LLMs) for code analysis, generation, and developer productivity Implementing AI-powered automated code review systems Applying predictive analytics to CI/CD pipelines for risk assessment and monitoring Building models for deployment failure prediction and reliability improvement Using generative AI for Infrastructure as Code (IaC) and automated provisioning With a strong focus on real-world applications, predictive intelligence, and AI integration, this book bridges the gap between software engineering, DevOps practices, and modern AI capabilities. Ideal for: DevOps engineers and platform engineers Software developers and architects AI/ML engineers working in software delivery systems Engineering managers and technical leaders Researchers and advanced students in software engineering This book equips readers with the knowledge and tools needed to design next-generation CI/CD pipelines that are intelligent, adaptive, and resilient by design.