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Duration 14 hours
Course Outline
Introduction to AI in DevOps
- Defining AI for DevOps
- Key use cases and advantages of AI within CI/CD pipelines
- Overview of tools and platforms that enable AI-driven automation
AI-Assisted Code Development and Review
- Leveraging GitHub Copilot and comparable tools for code completion
- AI-based code quality assessments and recommendations
- Automatic test generation and vulnerability detection
Intelligent CI/CD Pipeline Design
- Configuring Jenkins or GitHub Actions with AI-enhanced steps
- Predictive build triggering and intelligent rollback detection
- Dynamic pipeline adjustments informed by historical performance data
AI-Powered Testing Automation
- AI-driven test generation and prioritization (e.g., Testim, mabl)
- Regression test analysis utilizing machine learning
- Mitigating flakiness and optimizing test runtime through data-driven insights
Static and Dynamic Analysis with AI
- Integrating SonarQube and similar tools into pipelines
- Automated identification of code smells and refactoring recommendations
- Impact analysis and code risk profiling
Monitoring, Feedback, and Continuous Improvement
- AI-powered observability tools and anomaly detection
- Applying ML models to learn from deployment outcomes
- Establishing automated feedback loops throughout the SDLC
Case Studies and Practical Integration
- Real-world examples of AI-enhanced CI/CD in enterprise settings
- Integration with cloud-native platforms and microservices
- Addressing challenges, offering recommendations, and highlighting best practices
Summary and Next Steps
Requirements
- Proven experience with DevOps practices and CI/CD workflows
- Fundamental grasp of version control systems and automation tools
- Familiarity with software testing methodologies and deployment concepts
Target Audience
- DevOps engineers and platform teams
- QA automation leads and test engineers
- Software architects and release managers
