AI-Native Software Delivery. Proven Practices to Produce High-Quality Software Faster Nick Durkin, Eric Minick, Chinmay Gaikwad

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Spis treści książki

• Preface
  • Who Should Read This Book
  • Why We Wrote This Book
  • Navigating This Book
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. The Road to AI-Native DevOps
  • Development + Operations = DevOps
  • A Short History of DevOps
    • Agile in the Aughts
    • Continuous Integration and Continuous Delivery
    • Milestones in Early DevOps
    • DevOps 1.0
      • Challenges to DevOps 1.0
        • The adoption of cloud-native and microservices architectures
        • The rise of open source software
        • The importance of the digital experience and consumerization of enterprise
      • Outgrowing DevOps 1.0 toolsets
        • Widely used open source tools are often suboptimal
        • DIY pipelines result in redundant and wasteful efforts
        • Automation is often incomplete, requiring manual steps
        • Governance is an afterthought
    • DevOps 2.0
  • Summary
• 2. Source Control Management
  • Introducing Source Control Management
    • A Short History of Source Control Management
      • Distributed version control and Git
      • Branching out with Git
    • GitOps and Source Control Management
    • Monorepos and Remote Caching
    • AI in Source Control Management
  • Source Control Management in the Delivery Pipeline
  • Code Repository Considerations
    • Comprehensive Integrations
    • AI-Powered Features
    • Efficiency and Transparency Through Open Source
    • A Platform Approach
    • Access Control, an Example
    • Defining Roles, a Platform Approach
  • Summary
• 3. The Build and Pre-Deployment Testing Steps of Continuous Integration
  • A Short History of Building and Testing Software
    • Structured Software Development and Waterfall Methodologies
    • Agile and Test-Driven Development
    • Enter Continuous Integration
    • Continuous Integration Today
  • Continuous Integration in the CI/CD Pipeline
    • The Essential Build Step
    • Prioritizing Quality and Security with Static Analysis
    • Automated Testing: Test Early, Test Often
    • The Test Pyramid
  • Continuous Integration Tools
    • Jenkins Considered
      • Plug-in complexity
      • Scalability challenges
      • Security concerns
      • Resource usage and efficiency concerns
    • Beyond Jenkins
      • Hosting options
      • Mobile app developmentspecific challenges
    • Modern Features to Accelerate Software Builds
      • Accelerate builds with caching
      • Streamline building, caching, and testing with AI
        • Build phase enhancements
        • Cache phase enhancements
        • Test phase enhancements
      • AI-powered build and test insights
      • Unify CI/CD metrics with enterprise observability
      • Modern CI/CD support for monorepos
  • Summary
• 4. Deploying to Test Environments
  • Establishing a Unified Deployment Process
    • Deploy Consistently to Every Environment
      • Use consistent tooling
      • Use consistent pipeline steps and deployment strategies
      • Use parameterization for differences
      • Accelerate pipeline creation with AI
      • Leverage Infrastructure as Code for deployment consistency
    • Leverage Git Workflows with GitOps
  • Continuous Delivery, Deployment, and Test in the CI/CD Pipeline
    • Types of Testing
    • Intent-Based Functional and End-to-End Testing
    • Traditional Testing Versus a Hollowing-Out-the-Middle Approach
  • Promotion Between Environments
    • From Decisions by Committee to Automated Decisions
    • From Manual to Automated Promotion
    • Break the Environment Bottleneck
  • Summary
• 5. Securing Applications and the Software Supply Chain
  • Modern Applications and the Cyberthreat Landscape
    • The Growing Threat of Software Supply Chain Attacks
      • Applications risks in the software supply chain
      • DevOps risks in the software supply chain
      • A threat that is growing
    • Regulatory Compliance Frameworks That Apply to Software Supply Chains
  • Securing Modern Applications with Shift Left
    • The Need for Developer-Friendly Shift-Left Security
    • Application Security Scanners
  • Securing the Software Supply Chain
    • Identifying Top CI/CD Security Risks
    • Identifying Top OSS Risks
  • Ensuring Integrity with Supply Chain Levels for Software Artifacts
    • SLSA Overview
    • Using SLSA to Ensure Integrity
      • Enhancing supply chain security beyond SLSA
      • Addressing AI-generated dependency risks
  • Addressing Zero-Day Vulnerabilities with Software Bill of Materials
  • Using SBOMs to Remediate Dependency Issues
  • Adopting DevSecOps Principles
    • Establish a Collaborative Culture and Break Down Functional Silos
    • Adopt and Enforce Secure Coding Methodologies and Shift-Left
  • Summary
• 6. Chaos Engineering and Service Reliability
  • Getting Started with Chaos Engineering
    • Principles of Chaos Engineering
    • Starting Small and Scaling
      • Step 1: Conduct a simple latency experiment
      • Step 2: Test resilience against a more significant network issue
      • Step 3: Validate that the circuit breaker fails over to an alternative
    • Starting in Production-Like Environments
    • Leveraging Modern Tools
    • Learning from Others
      • Experiment 1: Control plane overload simulation
      • Experiment 2: DNS failure testing
      • Example 3: Break-glass access drills
  • Service-Level Objectives and Service Resiliency
    • Establishing Reliability Targets
    • Shared Ownership of System Reliability
  • Error Budgets and Their Role in Reliability and Innovation
    • Monitoring to Inform Chaos Testing Experiments
    • Strategic Use of Error Budgets for Chaos Testing Experiments
      • Prioritizing chaos experiments in alignment with your available error budget
      • Protect your error budget by utilizing AI-augmented dry runs and simulations
  • Integrating Chaos Engineering and SLOs into CI/CD Pipelines
    • Scaling Your Chaos Engineering Practices
    • Adding Chaos Engineering Experiments and SLOs to Your CI/CD Pipeline
    • Security and Governance for Chaos Engineering
  • The Future of AI-Native Chaos Engineering in Service Reliability
  • Summary
• 7. Deploying to Production
  • Governing Production Deployments
    • Traditional Approaches to Deployment Governance
    • Modern Approaches to Deployment Governance
      • Automating decision making
      • Building strong audit trails to automate compliance
      • Managing enforcement with Policy as Code
    • Safeguarding the Deployment Process
    • Future Trends in Deployment Governance
    • Reconciling Traditional and Modern Approaches
  • Production Deployment Strategies
    • The Traditional Big-Bang Deployment
    • Using a Progressive Delivery Strategy
      • Deploying rolling updates
      • Using blue-green deployments
      • Using canary releases
      • Using feature flags
      • Rolling back
      • Special considerations for specific architectures
    • Choosing the Right Tools
  • Verifying Production Deployments
    • Observability in Deployments
    • Modernizing the War Room
    • Testing Production Deployments
  • Summary
• 8. Feature Management and Experimentation
  • Benefits of Feature Management in Modern Software Development
    • Speeding Up Development Cycles with Feature Flags
    • Decoupling Teams to Reduce Coordination Overhead
    • Supporting Progressive Delivery with Phased Rollouts
    • Manage Tech Debt with Feature Flags
  • Optimizing Results Through Experimentation
    • Building Well-Structured Experiments
      • Strong, clear metrics
      • Targeted and randomized audience
      • A statistically significant sample size and experiment duration
      • Experiment separation
      • AI-powered interpretation
    • Integrating Experimentation with Progressive Delivery
    • Establishing Guardrails
  • Life Without Mature Feature Management Tools
    • Low-Quality Tools Impede Effective Feature Flag Management
    • Minimal Support for Experimentation Limits Your Learning
    • Lack of Integrations Slows You Down
    • Fragile Implementations Distract Your Team
  • Scaling Feature Management and Experimentation
    • Unify with a Single Feature Management Implementation
    • Reduce Manual Steps with Smart Integrations
    • Simplify Governance with Automated Audit Trails and Enforcement
    • Leverage Your Existing Identity Management Infrastructure
    • Choose a Platform Built to Scale
  • Summary
• 9. AI and Automation for Cloud Cost Management
  • The Evolution of Cloud Cost Management
    • Early Cloud Adoption and Initial Challenges
    • The Rise of FinOps
      • Core principles of FinOps
      • Phases of FinOps
        • Inform
        • Optimize
        • Operate
    • Modern Cloud Cost Management Challenges
  • AI-Driven Cloud Cost Optimization Strategies
    • Right-Sizing Cloud Resources
      • Cloud cost visibility required for the FinOps Inform phase
      • Moving from Inform to Optimize
    • Leveraging Commitment-Based Pricing and Spot Instances
      • Key considerations
      • How AI can help
    • Using AI to Manage Container Costs
    • Aligning Cost Savings Goals with Business Objectives
  • Automating Cloud Governance and Compliance
    • Implementing Cloud Governance Policies
    • Enforcing Budget Guardrails with Automation
    • Ensuring Tag Compliance Through Automation
    • Allocating the Costs of Shared Platforms and Services
  • Cloud Cost Management to Meet Environmental Sustainability Goals
  • The Future of AI in Cloud Cost Management
  • Summary
• 10. A Platform Engineering Approach to Modern DevOps
  • Why Platform Engineering?
    • The Developer Cognitive Load Crisis
    • From Toolchains to Platform as a Product
    • The Business Case for Platform Engineering
    • Supporting a Collaborative DevOps Culture
  • Creating and Operating Platform Teams
    • Critical Characteristics of a Platform Team
    • Engagement Models That Work
    • Effective Operating Models
  • Defining Your Platform Strategy
    • Setting Platform Principles
    • Platform Antiprinciples and Resolving Conflicts
    • Understanding Your Platform Audience
    • Selecting Platform Scope
    • A Practical Road Map Example
    • Balancing Standardization and Flexibility
  • Platform Measurement and Evolution
    • Measuring Platform Success
    • Driving Platform Adoption
    • Leveraging Internal Developer Portals for Platform Success
      • Core portal components
      • AI-enhanced developer experience
      • Building an effective portal
    • Sustainable Platform Evolution
    • A Practical Example: Platform Engineering in Action
      • Discovery and strategy development
      • Building the minimum viable platform
      • Expanding platform capabilities
      • Enterprise-wide adoption and business impact
      • Ongoing improvements
  • Conclusion
    • Looking Forward
    • Getting Started
• Index

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