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Course Outline
Introduction to Predictive AI in DevOps
- Fundamentals of Predictive AI
- The intersection of AI and DevOps
- Overview of predictive analytics in software delivery
Predictive Analytics and Modeling
- Understanding data-driven predictions
- Building predictive models for DevOps
- Tools and platforms for predictive analytics
AI-Driven Development Environments
- Setting up AI-enhanced development environments
- Predictive AI for coding and version control
- Integrating AI into continuous integration/continuous deployment (CI/CD) pipelines
Predictive AI in Testing and Quality Assurance
- AI for automated testing and error prediction
- Enhancing code quality with predictive insights
- Predictive models for performance and security testing
AI in Operations and Monitoring
- Predictive AI for system monitoring and alerts
- AI-driven root cause analysis
- Predictive maintenance and incident prevention
Case Studies and Best Practices
- Real-world applications of predictive AI in DevOps
- Best practices for implementing predictive AI
- Lessons learned from industry leaders
Workshop and Hands-On Labs
- Interactive sessions with predictive AI tools
- Simulations of predictive AI in DevOps scenarios
- Group projects on implementing predictive AI features
Ethical Considerations and Future Trends
- Ethical use of AI in DevOps
- Navigating the challenges of predictive AI
- Emerging trends and the future of AI in DevOps
Summary and Next Steps
Requirements
- An understanding of basic DevOps principles
- Experience with continuous integration and continuous deployment (CI/CD)
- Familiarity with data analytics and machine learning concepts
Audience
- DevOps engineers
- Software developers
- IT professionals
14 Hours