Kubiya AI Fundamentals Training is a practical two-day program designed to help DevOps, platform, and engineering teams understand how Kubiya AI enables conversational automation for DevOps operations. This course focuses on using Kubiya AI to interact with infrastructure, cloud services, CI/CD pipelines, and operational tools through natural language, helping teams improve productivity, reduce operational friction, and standardize DevOps workflows using AI-driven assistants.

• Understand Kubiya AI fundamentals
• Learn AI-powered DevOps automation concepts
• Understand workflow orchestration basics
• Gain knowledge of AI assistants for operations
• Learn infrastructure automation concepts
• Understand incident management workflows
• Explore AI-driven operational efficiency
• Identify enterprise automation use cases

• DevOps engineers and site reliability engineers
• Platform and cloud engineers
• IT operations and infrastructure teams
• Engineering managers and technical leads
• Organizations exploring AI-assisted DevOps automation

• Basic understanding of DevOps or IT operations concepts
• Familiarity with cloud platforms, CI/CD tools, or infrastructure workflows
• Comfort with command-line or operational tools is beneficial
• No prior AI or chatbot development experience required

• Understanding Kubiya AI capabilities and use cases
• Using conversational AI for DevOps and operational tasks
• Automating infrastructure and workflow interactions
• Improving operational efficiency with AI assistants
• Applying security and governance best practices in AI-driven DevOps

By the end of this training, participants will be able to explain Kubiya AI fundamentals, use conversational AI to perform DevOps and operational tasks, integrate Kubiya AI with existing tooling, and apply secure, governed, and reliable practices for AI-assisted DevOps operations.

Introduction to Kubiya AI and DevOps Automation
• Overview of Kubiya AI and its role in DevOps
• Conversational AI for infrastructure and operations
• Key benefits of AI-assisted DevOps workflows
• Common use cases across cloud and platform teams

Kubiya AI Architecture and Core Concepts
• Understanding how Kubiya AI connects to DevOps tools
• Role of agents, skills, and integrations
• Secure execution of operational commands
• Permissions, access control, and auditability

Using Kubiya AI for Everyday DevOps Tasks
• Executing infrastructure commands via chat
• Querying system status and logs
• Automating repetitive operational workflows
• Improving collaboration through shared AI assistants

Integrations with Cloud and DevOps Tooling
• Integrating Kubiya AI with cloud platforms
• Working with CI/CD pipelines and deployment tools
• Supporting monitoring, alerting, and incident workflows
• Extending Kubiya AI with custom skills

Security, Governance, and Reliability
• Role-based access and command approval flows
• Preventing misuse and operational risks
• Logging, auditing, and compliance considerations
• Reliability and trust in AI-driven operations

Real-World Use Cases and Best Practices
• Incident response and operational troubleshooting
• Self-service DevOps for engineering teams
• Standardizing operational knowledge
• Scaling Kubiya AI adoption across teams

• Kubiya AI platform overview
• AI-driven DevOps concepts
• Workflow automation basics
• Infrastructure orchestration concepts
• Incident management workflows
• AI assistants for operations
• Integration and deployment basics
• Security and access control concepts
• Enterprise automation use cases
• Practical Kubiya AI scenarios

• Hands-on Kubiya AI workflow exercises
• DevOps automation use case assessment
• Security and governance scenario evaluation
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in Kubiya AI Fundamentals Training, validating their expertise in using Kubiya AI for conversational DevOps automation, operational efficiency, and secure AI-driven workflows.