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 Duration 14 hours

Course Outline

Introduction to CI/CD Pipelines and Kubiya AI

  • Overview of CI/CD concepts and workflows
  • Overview of Kubiya AI and its function in DevOps automation
  • Examination of Kubiya AI’s primary features

Integrating Kubiya AI with Leading CI/CD Tools

  • Configuring Kubiya AI with Jenkins
  • Integration of Kubiya AI with GitLab CI
  • Linking Kubiya AI with Docker-based pipelines

Automating CI/CD Pipeline Tasks via Kubiya AI

  • AI-assisted automation for build, testing, and deployment phases
  • Minimizing manual effort through AI automation
  • Optimizing pipeline management and troubleshooting processes

Supervising and Managing CI/CD Pipelines with AI

  • Real-time oversight of pipeline status
  • Early detection of issues through AI analytics
  • Automated alerts and issue resolution workflows

Advanced AI Applications in CI/CD Pipelines

  • AI-led optimization of resource distribution
  • Predictive analysis for potential pipeline failures
  • AI-based anomaly detection within CI/CD pipelines

Strengthening CI/CD Pipeline Security with AI

  • Utilizing AI to identify security vulnerabilities
  • Improving code review procedures using AI
  • Maintaining compliance via automated AI-driven inspections

Scaling CI/CD Pipelines with AI

  • Leveraging AI to manage extensive DevOps environments
  • Automating the scaling of CI/CD infrastructure
  • Real-world examples of AI-facilitated scalability in production

Summary and Future Directions

Requirements

  • Foundational knowledge of CI/CD pipelines
  • Practical experience with DevOps tools (such as Jenkins, GitLab)
  • Acquaintance with automation processes

Target Audience

  • DevOps engineers
  • CI/CD pipeline managers
  • Infrastructure automation specialists

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