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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
Testimonials (1)
There were many practical exercises supervised and assisted by the trainer