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Modern DevOps for Cloud and AI Platforms

Architecting, Operating, and Scaling Reliable Software Systems in the Age of AI

Modern DevOps for Cloud and AI Platforms
This book is 100% completeLast updated on 2026-07-31
Steve Publications

Cloud, Kubernetes, AI, security and automation have changed what DevOps really means. This book cuts through the hype with practical guidance for building reliable, scalable platforms using proven patterns, real production lessons and modern engineering practices that help teams move faster without sacrificing stability.

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About the Book

Modern software delivery has evolved far beyond the original promise of DevOps. Today's engineering organizations face a fundamentally different landscape: cloud-native architectures running at planetary scale, artificial intelligence workloads reshaping infrastructure demands, security threats that follow every supply chain link, and the relentless pressure to deliver value faster than ever before. This book maps the full terrain of modern DevOps practice for senior engineers, platform architects, SREs, and technology leaders who build and operate the systems that power contemporary businesses. You will find deep technical guidance on cloud-native architectures, Kubernetes operations, GitOps workflows, observability engineering, security integration, AI infrastructure, cost optimization, and organizational strategies that turn DevOps from a buzzword into a competitive advantage. Every chapter is grounded in real-world patterns, production-tested practices, and the latest research on what actually works at scale.

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DevOpsInfrastructure as CodeSystems AdministrationSystems EngineeringCloud ComputingArtificial Intelligence

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About the Author

Steve Publications

Steve is a technology professional with over 20 years of experience in software development, server infrastructure, cybersecurity, vulnerability research and reverse engineering. He specializes in analyzing complex systems, identifying weaknesses and improving security.

Beyond his professional work, Steve collaborates with researchers and industry experts to create practical technical books covering software development, cybersecurity, cloud computing, DevOps, AI and enterprise technologies. His goal is to help IT professionals, students and enthusiasts develop practical skills through clear explanations, real-world examples and hands-on exercises.

At Steve Publications, we use AI to assist with translation, proofreading and editing but not to replace the research, technical expertise or hands-on experience behind our books. Some authors publish under pseudonyms for privacy or professional reasons.

Every book is written, reviewed and maintained by experienced professionals, supported by a private community of over 420 engineers and researchers. We prioritize technical accuracy, practical value and regularly updated content over mass-produced AI-generated text.

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Contents

Table of Contents

Architecting, Operating, and Scaling Reliable Software Systems in the Age of AI

Introduction: The New Reality of Software Delivery

Chapter 1: The Evolution and Essence of Modern DevOps

  1. From Silos to Systems: The Birth of DevOps
  2. Cultural Foundations and Technical Practices
  3. The DevOps Maturity Spectrum in Modern Enterprises
  4. Why Cloud and AI Changed Everything

Chapter 2: Cloud-Native Architecture Fundamentals

  1. Containers and the Runtime Revolution
  2. Kubernetes as the Operating System of Cloud Native
  3. Microservices and Distributed System Design
  4. Cloud-Native Design Patterns and Anti-Patterns

Chapter 3: Infrastructure as Code and GitOps

  1. Declarative Infrastructure and IaC Principles
  2. Provisioning at Scale with Terraform and Alternatives
  3. Configuration Management in Cloud-Native Environments
  4. GitOps: Continuous Delivery for Infrastructure

Chapter 4: CI/CD and Release Engineering at Scale

  1. Pipeline Architecture and Design Patterns
  2. Testing Strategies for Continuous Delivery
  3. Advanced Deployment Techniques and Rollback
  4. Scaling CI/CD Across Hundreds of Teams

Chapter 5: Platform Engineering and Developer Experience

  1. The Rise of Platform Engineering
  2. Designing Internal Developer Platforms
  3. Golden Paths and Self-Service Capabilities
  4. Measuring and Improving Developer Experience

Chapter 6: DevSecOps and Software Supply Chain Security

  1. Shifting Left Without Slowing Down
  2. Software Supply Chain Security and SBOMs
  3. Policy as Code and Guardrails
  4. Zero Trust Architecture for Cloud Platforms

Chapter 7: Identity, Access, and Secrets Management

  1. Identity in Distributed Systems
  2. Role-Based and Attribute-Based Access Control
  3. Secrets Management Patterns and Tools
  4. Service Meshes and mTLS Communication

Chapter 8: Observability, SRE, and Reliability Engineering

  1. The Three Pillars: Metrics, Logs, and Traces
  2. Distributed Tracing in Microservice Architectures
  3. SRE Practices, SLIs, SLOs, and Error Budgets
  4. Building Observability into Platform Design

Chapter 9: Resilience Engineering and Incident Management

  1. Designing for Failure and Fault Tolerance
  2. Chaos Engineering and Resilience Testing
  3. Incident Management and On-Call Operations
  4. Postmortems, Learning, and Continuous Improvement

Chapter 10: FinOps, Governance, and Multi-Cloud Strategy

  1. FinOps Principles and Cloud Cost Optimization
  2. Governance, Compliance, and Audit Readiness
  3. Multi-Cloud and Hybrid Cloud Architectures
  4. Risk Management and Disaster Recovery

Chapter 11: Data Engineering and MLOps Foundations

  1. Data Pipelines as First-Class Infrastructure
  2. MLOps Lifecycle and Model Operations
  3. Feature Stores and Experiment Tracking
  4. Monitoring ML Models in Production

Chapter 12: LLMOps and AI Infrastructure

  1. The LLMOps Paradigm and Its Differences from MLOps
  2. GPU Orchestration and AI Workload Scheduling
  3. Inference Platforms and Serving Architectures
  4. Vector Databases and AI Application Infrastructure

Chapter 13: AI-Assisted Development and AIOps

  1. AI-Assisted Software Development Workflows
  2. Automated Code Generation and Review
  3. AIOps: Intelligent Operations and Anomaly Detection
  4. The Future of AI in DevOps Practices

Chapter 14: The Future of DevOps and Synthesis

  1. Emerging Architectures and Technologies
  2. Platform-as-a-Product and Internal Marketplaces
  3. The Evolving Role of the DevOps Engineer
  4. A Practical Roadmap for Your Organization

Conclusion: Delivering on the Promise of Modern DevOps

References

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