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Course Outline
Introduction to AI in DevOps
- Defining AI for DevOps
- Real-world use cases and benefits of AI in CI/CD pipelines
- Overview of tools and platforms that support AI-driven automation
AI-Assisted Code Development and Review
- Utilizing GitHub Copilot and comparable tools for code completion
- AI-driven code quality checks and recommendations
- Automated test generation and vulnerability detection
Intelligent CI/CD Pipeline Design
- Configuring Jenkins or GitHub Actions with AI-enhanced steps
- Predictive build triggering and smart rollback detection
- Dynamic pipeline adjustments based on historical performance
AI-Powered Testing Automation
- AI-driven test generation and prioritization (e.g., Testim, mabl)
- Regression test analysis using machine learning
- Reducing flakiness and test runtime with data-driven insights
Static and Dynamic Analysis with AI
- Integrating SonarQube and similar tools into pipelines
- Automated detection of code smells and refactoring suggestions
- Impact analysis and code risk profiling
Monitoring, Feedback, and Continuous Improvement
- AI-powered observability tools and anomaly detection
- Using ML models to learn from deployment outcomes
- Creating automated feedback loops across the SDLC
Case Studies and Practical Integration
- Examples of AI-enhanced CI/CD in enterprise environments
- Integrating with cloud-native platforms and microservices
- Challenges, recommendations, and best practices
Summary and Next Steps
Requirements
- Hands-on experience with DevOps and CI/CD workflows
- Foundational knowledge of version control and automation tools
- Familiarity with software testing and deployment concepts
Audience
- DevOps engineers and platform teams
- QA automation leads and test engineers
- Software architects and release managers
14 Hours