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Duration 14 hours
Course Outline
Foundations of AI in DevOps
- Defining the scope of AI in DevOps.
- Exploring the use cases and advantages of AI within CI/CD pipelines.
- Surveying tools and platforms that support AI-driven automation.
AI-Enhanced Code Development and Review
- Leveraging GitHub Copilot and analogous tools for intelligent code completion.
- Applying AI-based checks and recommendations for code quality.
- Automating test generation and vulnerability detection.
Designing Intelligent CI/CD Pipelines
- Configuring Jenkins or GitHub Actions with AI-augmented stages.
- Implementing predictive build triggers and smart rollback detection.
- Enabling dynamic pipeline adjustments driven by historical performance data.
AI-Driven Testing Automation
- Utilizing AI for test generation and prioritization (e.g., Testim, mabl).
- Analyzing regression tests through machine learning models.
- Mitigating flakiness and reducing test runtime via data-driven insights.
AI-Powered Static and Dynamic Analysis
- Incorporating SonarQube and comparable tools into pipeline workflows.
- Automating the identification of code smells and refactoring opportunities.
- Conducting impact analysis and code risk profiling.
Monitoring, Feedback, and Continuous Improvement
- Deploying AI-powered observability solutions and anomaly detection.
- Using ML models to derive insights from deployment outcomes.
- Establishing automated feedback loops across the SDLC.
Case Studies and Practical Application
- Examining real-world examples of AI-enhanced CI/CD in enterprise settings.
- Integrating solutions with cloud-native platforms and microservices architectures.
- Addressing challenges, recommendations, and industry best practices.
Key Takeaways and Future Directions
Requirements
- Practical experience with DevOps practices and CI/CD workflows.
- Foundational knowledge of version control systems and automation utilities.
- Familiarity with software testing principles and deployment methodologies.
Target Audience
- DevOps engineers and platform engineering teams.
- QA automation leads and test engineers.
- Software architects and release managers.
