Course Outline

Introduction to CI/CD Pipelines and Kubiya AI

  • Overview of CI/CD concepts and processes
  • Introduction to Kubiya AI and its role in DevOps automation
  • Exploring key features of Kubiya AI

Integrating Kubiya AI with Popular CI/CD Tools

  • Setting up Kubiya AI with Jenkins
  • Integrating Kubiya AI with GitLab CI
  • Connecting Kubiya AI with Docker-based pipelines

Automating CI/CD Pipeline Tasks with Kubiya AI

  • AI-powered automation for build, test, and deploy stages
  • Reducing manual intervention with AI automation
  • Streamlining pipeline management and troubleshooting

Monitoring and Managing CI/CD Pipelines Using AI

  • Real-time monitoring of pipeline health
  • Proactive issue detection using AI analytics
  • Automated notifications and problem resolution workflows

Advanced AI Applications in CI/CD Pipelines

  • AI-driven optimization for resource allocation
  • Predictive analytics for pipeline failures
  • AI-based anomaly detection in CI/CD pipelines

CI/CD Pipeline Security Enhancement with AI

  • Leveraging AI for detecting security vulnerabilities
  • Enhancing code review processes using AI
  • Ensuring compliance with automated AI-driven checks

Scaling CI/CD Pipelines with AI

  • Using AI to manage large-scale DevOps environments
  • Automating scaling of CI/CD infrastructure
  • Case studies of AI-enabled scalability in production

Summary and Next Steps

Requirements

  • Basic understanding of CI/CD pipelines
  • Experience with DevOps tools (e.g., Jenkins, GitLab)
  • Familiarity with automation processes

Audience

  • DevOps engineers
  • CI/CD pipeline managers
  • Infrastructure automation professionals
 14 Hours

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Provisional Upcoming Courses (Require 5+ participants)

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