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Duration 14 hours
Course Outline
Introduction to AI in DevOps
- Defining AI in the DevOps context
- Use cases and advantages of AI in CI/CD pipelines
- Overview of tools and platforms that support AI-driven automation
AI-Assisted Code Development and Review
- Leveraging GitHub Copilot and similar 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 data
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 through 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
- Utilizing ML models to learn from deployment outcomes
- Establishing automated feedback loops across the SDLC
Case Studies and Practical Integration
- Examples of AI-enhanced CI/CD in enterprise environments
- Integration with cloud-native platforms and microservices
- Challenges, recommendations, and best practices
Summary and Next Steps
Requirements
- Proficiency in DevOps and CI/CD workflows
- Fundamental knowledge of version control and automation tools
- Familiarity with software testing and deployment principles
Target Audience
- DevOps engineers and platform teams
- QA automation leads and test engineers
- Software architects and release managers
