Chapter 1: Introduction to Microservices
- 1.1 Understanding Microservices
- 1.2 Principles of Microservices
- 1.3 Benefits and Challenges
Chapter 2: Microservices Design Patterns Overview
- 2.1 Importance of Design Patterns
- 2.2 Categories of Design Patterns
- 2.3 Selecting and Combining Patterns
Chapter 3: Service Decomposition Patterns
- 3.1 Decompose by Business Capability
- 3.2 Domain-Driven Design (DDD)
- 3.3 Strangler Pattern
- 3.4 Evolutionary Architecture
Chapter 4: Structural and Architectural Patterns
- 4.1 Aggregator Pattern
- 4.2 Proxy Pattern
- 4.3 Chained Microservice Pattern
- 4.4 Branch Pattern
- 4.5 Sidecar Pattern
- 4.6 Ambassador Pattern
- 4.7 Adapter Pattern
- 4.8 Anti-Corruption Layer Pattern
Chapter 5: Communication Patterns
- 5.1 Inter-Service Communication Overview
- 5.2 API Gateway Pattern
- 5.3 Service Discovery Patterns
- 5.4 Messaging and Event-Driven Communication
- 5.5 Service Mesh and Advanced Communication
Chapter 6: API Design and Management Patterns
- 6.1 API Design Principles
- 6.2 API Versioning Strategies
- 6.3 API Composition and Aggregation
- 6.4 API Documentation and Discoverability
Chapter 7: Data Management and Consistency Patterns
- 7.1 Database per Service Pattern
- 7.2 Saga Pattern
- 7.3 Command Query Responsibility Segregation (CQRS)
- 7.4 Event Sourcing
- 7.5 Data Consistency and Transactions
- 7.6 Data Partitioning and Sharding
Chapter 8: Resilience and Fault Tolerance Patterns
- 8.1 Designing Resilient Systems
- 8.2 Fault Tolerance Patterns
- 8.3 Isolation Patterns
- 8.4 Load Management
- 8.5 Chaos Engineering
Chapter 9: Deployment and Operational Patterns
- 9.1 Containerization and Orchestration
- 9.2 Deployment Strategies
- 9.3 Infrastructure as Code
- 9.4 Auto-Scaling and Performance
Chapter 10: Configuration Management Patterns
- 10.1 Externalized Configuration
- 10.2 Centralized Configuration
- 10.3 Feature Toggles and Flags
- 10.4 Service Configuration Synchronization
Chapter 11: Observability and Monitoring Patterns
- 11.1 The Three Pillars of Observability
- 11.2 Implementing Observability
- 11.3 Alerting and Incident Response
- 11.4 Enhancing Observability
Chapter 12: Security Patterns
- 12.1 Securing Microservices
- 12.2 Authentication and Authorization
- 12.3 Secure Communication
- 12.4 Secret Management and Compliance
- 12.5 Advanced Security Patterns
Chapter 13: Testing Patterns
- 13.1 Testing Strategies for Microservices
- 13.2 Consumer-Driven Contract Testing
- 13.3 Service Virtualization and Mocking
- 13.4 Continuous Testing and Automation
- 13.5 Chaos Engineering in Testing
Chapter 14: Governance and Organizational Patterns
- 14.1 Importance of Governance
- 14.2 API Governance
- 14.3 Service Lifecycle Management
- 14.4 Policy Enforcement and Cultural Aspects
- 14.5 Organizational Structures
Chapter 15: Migration Strategies and Patterns
- 15.1 Planning the Migration
- 15.2 Migration Patterns
- 15.3 Data Migration
- 15.4 Decommissioning Legacy Systems
- 15.5 Lessons Learned
Chapter 16: Future Trends and Advanced Topics
- 16.1 Serverless Microservices
- 16.2 Edge Computing and Microservices
- 16.3 Reactive Systems and Event Streaming
- 16.4 Micro Frontends
- 16.5 Machine Learning and AI in Microservices
Chapter 17: Case Studies
- 17.1 E-Commerce Platform Transformation
- 17.2 Financial Services Security Implementation
- 17.3 Media Streaming Service Scaling
- 17.4 Logistics and Supply Chain Optimization
- 17.5 Summary and Key Takeaways
Chapter 18: Conclusion
- 18.1 Recap of Key Concepts
- 18.2 Best Practices Summary
- 18.3 Navigating Microservices Complexity
Appendix A: Tools and Technologies
- A.1 Containerization and Orchestration
- A.2 Messaging and Event Streaming
- A.3 Monitoring and Logging
- A.4 Security and Testing Tools
Appendix B: Glossary of Terms
- B.1 Key Microservices Terms
- B.2 Acronyms and Abbreviations
Appendix C: Additional Resources
- C.1 Books and Publications
- C.2 Online Courses and Tutorials
- C.3 Community and Conferences
- C.4 Open Source Projects

16.5 Machine Learning and AI in Microservices

In this section

Incorporating AI Services in Microservices: AI Integration, Use Cases, and Best Practices
Explore how to incorporate AI services into microservices architectures, focusing on AI use cases, frameworks, scalable models, independent deployment, data privacy, integration with data pipelines, and model maintenance.
Data Pipelines in Microservices: Building Scalable and Efficient Workflows
Explore the intricacies of designing and implementing data pipelines in microservices architectures, focusing on scalability, data quality, and security.
Ethical Considerations in AI for Microservices
Explore ethical considerations in AI for microservices, focusing on fairness, accountability, transparency, privacy, and inclusivity.

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16.4 Micro Frontends

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16.5 Machine Learning and AI in Microservices

In this section