This book is a timely and authoritative guide for platform and DevOps engineers navigating the rapid convergence of AI and operations. As traditional responsibilities expand beyond infrastructure and CI/CD pipelines, engineers must now design and operate intelligent systems from anomaly detection pipelines and ML models to LLM-powered applications. This book provides the first unified framework that brings together AIOps, MLOps, and LLMOps, translating complex AI concepts into practical, production-ready strategies. Grounded in over a decade of real-world experience and reinforced by peer-reviewed research, it equips readers with the knowledge to build scalable, intelligent platforms using open, vendor-neutral tooling. Structured across five comprehensive parts, the book progresses from foundational concepts to practical implementations. It begins with platform engineering fundamentals, OpenTelemetry-based observability, and AI-assisted infrastructure as code. It then dives into AIOps, covering anomaly detection, ML-driven FinOps, and AI-powered chaos engineering. The MLOps section walks through the complete model lifecycle pipelines, serving, monitoring, and drift detection, tailored specifically for platform engineers. The LLMOps section explores prompt management, RAG architectures, DevOps tooling powered by LLMs, and governance practices for secure AI systems. The final section integrates these disciplines into a unified platform architecture, complete with Kubernetes-based reference implementations, migration strategies, and organizational best practices. Each chapter includes hands-on examples in Python, Kubernetes, and Terraform, along with measurable benchmarks and reproducible projects. By the end of this book, readers will be able to design, build, and operate a fully integrated intelligent operations platform that unifies AIOps, MLOps, and LLMOps. They will gain practical skills to deploy AI-driven systems at scale, implement observability pipelines as ML
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