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Course Outline

Introduction to Kubiya AI

  • Overview of Kubiya AI capabilities
  • Utilizing AI for automation in cloud infrastructure
  • Core features of Kubiya AI for cloud management

Cloud Resource Automation

  • Automated provisioning of resources using Kubiya AI
  • Governance of cloud environments via AI-driven workflows
  • Integration with major cloud providers (AWS, Azure, Google Cloud) for government operations

Optimizing Cloud Expenditures

  • AI-based strategies for cost efficiency
  • Continuous monitoring of cloud utilization and spending
  • Data-driven recommendations for reducing cloud expenses

Security Enhancements with Kubiya AI

  • Threat detection and security monitoring powered by AI
  • Automated incident response protocols
  • Enforcement of compliance standards through AI mechanisms

Practical Application of Kubiya AI

  • Configuration of Kubiya AI for cloud operations
  • Lab exercises: Automating resource management
  • Lab exercises: Deploying cost optimization strategies

Operational Challenges and Future Trends

  • Addressing scalability and performance limitations in AI automation
  • Emerging developments in AI for cloud operations
  • The evolving landscape of AI-driven cloud governance

Advanced Kubiya AI Concepts

  • Exploration of sophisticated AI features for automation
  • Deployment of advanced security and fiscal responsibility techniques
  • Tailoring Kubiya AI to specific cloud architectures

Summary and Next Steps

Requirements

  • Proficiency with cloud infrastructure services, including AWS, Microsoft Azure, or Google Cloud Platform.
  • Foundational understanding of DevOps methodologies and continuous integration/continuous deployment (CI/CD) pipelines.
  • Experience utilizing automation frameworks to streamline operational workflows.

Audience

  • Cloud infrastructure engineers
  • IT operations leadership
 14 Hours

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