Generative AI for DevOps with GitHub, Azure OpenAI & Agents

About this course

Generative AI is quickly becoming part of modern DevOps work, but using AI well requires more than asking ChatGPT to write a YAML file.

If you work with pipelines, deployments, infrastructure, release notes, testing, documentation, or incident response, you have probably already seen how tools like GitHub Copilot, Azure OpenAI, ChatGPT, and AI agents can speed up technical work. The real challenge is knowing how to use these tools responsibly, especially when your work affects CI/CD workflows, cloud infrastructure, security, and production systems.

This course helps you apply Generative AI for DevOps in a practical, professional, and responsible way.

You will learn how to use GitHub Copilot, Azure OpenAI, GitHub Actions, AI agents, MCP, Bicep, and .NET to support real DevOps workflows. The focus is not hype or generic prompting. The focus is on helping you understand where AI can improve DevOps outcomes, where it should be constrained, and how to keep human review, security, and accountability at the center of your process.

You will start with the foundations of Generative AI for DevOps. You will explore where AI adds value across the DevOps lifecycle, including planning, coding, CI/CD, Infrastructure as Code, testing, release management, documentation, ChatOps, and incident response. You will also learn the difference between AI-assisted DevOps and AI-autonomous DevOps, including the risks teams must manage, such as hallucinations, drift, overreach, and unsafe automation.

From there, the course moves into practical, production-relevant scenarios. You will use AI to support Infrastructure as Code, review and improve GitHub Actions workflows, generate release notes, strengthen test strategy, support root cause analysis, and assist with incident response. These lessons are designed to help you move beyond simple code generation and start using AI as a structured assistant for real e

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