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Course Outline
Introduction to Kubiya AI and Enterprise DevOps
- Overview of Kubiya AI's capabilities for enterprise-scale DevOps
- Challenges in automating enterprise DevOps
- The role of AI in modern enterprise infrastructure
Advanced Customization of Kubiya AI
- Configuring Kubiya AI for enterprise workflows
- Customizing AI-driven pipelines for enterprise needs
- Implementing custom rules and automation logic
Integrating Kubiya AI with CI/CD Toolchains
- Connecting Kubiya AI with Jenkins, GitLab, Ansible, and more
- AI-enhanced pipeline monitoring and management
- Handling enterprise-specific CI/CD use cases
Enhancing Security and Compliance with Kubiya AI
- AI-driven security checks and threat detection
- Ensuring compliance with enterprise policies using AI
- Securing data and access within AI-driven workflows
Scaling DevOps Automation with Kubiya AI
- Optimizing resource allocation through AI
- Automating enterprise-wide deployments
- Scaling AI-driven DevOps across multiple environments
Monitoring and Optimizing DevOps Pipelines
- Using Kubiya AI for real-time pipeline monitoring
- AI-driven optimization of workflows and processes
- Minimizing downtime and automating incident responses
Future Trends in AI for Enterprise DevOps
- Emerging AI technologies in DevOps automation
- Challenges and opportunities in large-scale AI integration
- Future outlook for AI-driven enterprise DevOps
Summary and Next Steps
Requirements
- Advanced knowledge of DevOps practices and tools
- Experience with CI/CD pipeline management
- Familiarity with enterprise infrastructure and security protocols
Audience
- Enterprise DevOps teams
- IT architects
14 Hours
Testimonials (1)
There were many practical exercises supervised and assisted by the trainer