What you'll learn:
- Apply Generative AI across the DevOps lifecycle for coding, pipelines, infrastructure, testing, release management, and incident response.
- Utilize Copilot to streamline CI/CD workflows.
- Automate repetitive DevOps tasks with Copilot.
- Enhance infrastructure as code (IaC) practices.
- Troubleshoot and monitor with AI assistance.
- Develop DevOps tuned AI Agents
- Implemented Autonomous AI Agents
- Integreate LLM Reasoning into GitHub Workflows
- Understand the difference between AI-assisted and AI-autonomous DevOps, including safe and unsafe use cases.
- Use AI for ChatOps, release notes, test strategy, root cause analysis, and documentation workflows.
- Add observability, evaluation, and feedback loops to AI-assisted pipelines and agents.
- Improve DevOps outcomes with better prompt engineering and context engineering for operational tasks.
- Design deterministic and secure AI-assisted workflow steps for GitHub Actions and pipeline automation.
- Build and extend a minimal DevOps agent, add memory carefully, and integrate MCP-based tooling into workflows.
- Establish enterprise guardrails for security, compliance, accountability, and responsible AI use in DevOps teams.
Generative AI is changing how modern DevOps teams build, deploy, secure, and operate software. This course shows you how to apply Generative AI to real DevOps work using Azure DevOps concepts, GitHub, GitHub Actions, Azure OpenAI, Bicep, and .NET.
You will start with the foundations of Generative AI for DevOps, including where AI adds value across the DevOps lifecycle, the difference between AI-assisted and AI-autonomous workflows, and the practical risks teams must manage, such as hallucinations, drift, and overreach. From there, the course moves into hands-on, production-relevant scenarios like AI-assisted Infrastructure as Code, pipeline authoring, release note generation, test strategy improvement, incident response support, and ChatOps integration.
You will also learn how to integrate AI responsibly into CI/CD systems by designing deterministic workflow steps, orchestrating AI jobs in GitHub Actions, improving context quality, managing cost and performance, and adding observability and feedback loops. The course then expands into enterprise concerns such as AI tooling strategy, guardrails, auditing, accountability, ROI, and adoption planning.
Finally, you will explore advanced agentic AI for DevOps, including what makes an AI agent different, where agentic patterns fit in CI/CD, how to build a minimal DevOps agent, how to add memory safely, how to standardize tool access with MCP, how to extend workflows with GitHub tooling, and how to observe and evaluate agents in practice.
This course is designed for professionals who want practical, enterprise-relevant skills, not hype. You will learn where AI genuinely improves DevOps outcomes, where it should be constrained, and how to apply it in ways that are useful, measurable, and responsible.
