Struttura del corso

Introduction to AI in DevOps

  • What is AI for DevOps?
  • Use cases and benefits of AI in CI/CD pipelines
  • Overview of tools and platforms supporting AI-driven automation

AI-Assisted Code Development and Review

  • Using GitHub Copilot and similar tools for code completion
  • AI-based code quality checks and suggestions
  • Generating tests and detecting vulnerabilities automatically

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

Requisiti

  • Esperienza con DevOps e flussi di lavoro CI/CD
  • Comprensione basilare dei sistemi di controllo delle versioni e degli strumenti di automazione
  • Familiarità con i concetti di testing e deploy del software

Pubblico Obiettivo

  • Ingegneri DevOps e team di piattaforme
  • Responsabili di automazione QA e ingegneri di test
  • Architetti software e manager di rilascio
 14 ore

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