Azure AI Services for DevOps Engineers

About the Course

This advanced hands-on seminar introduces participants to the integration of Azure AI services within DevOps environments.
Throughout the day, participants will learn how to build intelligent, automated pipelines using Azure Machine Learning, Azure OpenAI, and Cognitive Services—and how to connect them to enterprise resources such as Azure DevOps (ADO), GitHub, and internal infrastructure.

The seminar focuses on how AI can assist developers and DevOps teams in:

  • Automating code reviews and deployment validation

  • Enhancing CI/CD processes with AI-driven insights

  • Querying and summarizing code repositories with OpenAI and GitHub Copilot

  • Building secure, scalable environments for AI integration across the organization

Target Audience

DevOps Engineers, Cloud Architects, AI Engineers, Software Developers, and Automation Specialists seeking to enhance their Azure environments with AI-driven capabilities.

Prerequisites

  • Working knowledge of Azure DevOps or GitHub Actions

  • Basic understanding of Azure infrastructure and networking

  • Familiarity with scripting (PowerShell, Python, or Bash)

Topics Covered

1. Introduction to AI in DevOps

  • What is AI-driven DevOps (AIOps)?

  • Microsoft’s AI ecosystem for cloud automation

  • When and how to use Cognitive Services, OpenAI, and Azure ML in DevOps

2. Building the AI Infrastructure

  • Designing Azure AI architecture for CI/CD environments

  • Securing access using Azure Entra ID, Key Vault, and Private Endpoints

  • Deploying resources via Terraform / Bicep

  • Governance and cost control for AI workloads

3. Connecting Azure AI to Company Resources

  • Integration with Azure Monitor and Application Insights

  • Collecting operational data for AI models

  • Querying enterprise data via Cognitive Search and Data Factory

  • Using AI for anomaly detection and root cause analysis in production

4. AI Meets Code: ADO, GitHub, and OpenAI

  • Accessing repositories from Azure DevOps

  • Connecting GitHub repositories and using GitHub Copilot for automated insights

  • Querying codebases with OpenAI (semantic search and GPT models)

  • Live Demo: Ask GPT to summarize issues and propose fixes from ADO

5. Automating Workflows with Logic Apps & Functions

  • Building serverless pipelines that trigger based on repository changes

  • Auto-validation and self-healing pipelines

  • Integration with Teams, ServiceNow, and ticketing systems

6. Practical Workshop

  • Create a DevOps pipeline that uses OpenAI to analyze deployment logs

  • Build a knowledge bot that answers questions about your code repositories

  • Deploy AI models securely using GitHub Actions or Azure DevOps pipelines

7. Best Practices and Real Use Cases

  • Responsible AI in DevOps operations

  • Security considerations for code and data access

  • Case study: AI-enhanced deployment pipeline in a real enterprise

Learning Outcomes

By the end of the course, participants will be able to:

  • Design and deploy AI-integrated DevOps environments

  • Automate monitoring, debugging, and code analysis using Azure AI

  • Connect OpenAI, GitHub, and Azure resources in a secure, scalable workflow