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AI DevOps — Build Smarter, Deploy Faster
Build Intelligent DevOps Solutions Using LLMs, RAG, Agentic AI, MCP, Terraform, Azure, and CI/CD Automation
Created byRohit Kumar Singh
Last updated 8/2026
English
What you'll learn
- Understand the fundamentals of Artificial Intelligence (AI), Generative AI, Large Language Models (LLMs), and how they are transforming modern DevOps practices.
- Build effective prompts using Prompt Engineering techniques to automate DevOps tasks such as CI/CD pipelines, Infrastructure as Code, T/S, and documentation.
- Develop intelligent AI agents using CrewAI to automate complex DevOps workflows and multi-step operational tasks.
- Understand & implement Model Context Protocol (MCP) to enable AI assistants to securely interact with GitHub, Azure DevOps, Terraform, and other external tool.
- Integrate AI with GitHub and Azure DevOps to enhance source code management, pull requests, pipelines, and software delivery workflows.
- Use Terraform MCP to explore AI-assisted Infrastructure as Code (IaC) workflows and infrastructure management.
- Install, configure, and run open-source Large Language Models locally using Ollama for private and cost-effective AI development.
- Explore Azure AI Services and LLM platforms to build AI-powered DevOps solutions using enterprise-grade cloud services.
- Implement AI-driven observability, monitoring, incident analysis, and automated remediation using Azure SRE Agent concepts.
- Apply AI across real-world DevOps scenarios to improve deployment speed, operational efficiency, troubleshooting, and overall platform reliability.
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Course content
11 sections • 96 lectures • 10h 4m total length
Requirements
- No prior experience with Artificial Intelligence or Large Language Models (LLMs) is required. This course starts with the fundamentals and gradually progresses to advanced DevOps AI concepts.
- Basic understanding of DevOps concepts such as CI/CD, version control, Infrastructure as Code (IaC), and cloud computing is recommended.
- An Azure subscription (Free Tier or Pay-As-You-Go) is recommended for Azure AI Services and Azure DevOps demonstrations, but alternative approaches are discussed where possible.
- Willingness to install free tools such as Visual Studio Code, Git, Docker, Python, Ollama, and other utilities used during the course.
- A computer running Windows, macOS, or Linux with internet access for installing the required tools and completing hands-on exercises.
- Curiosity and a willingness to explore how Artificial Intelligence can enhance modern DevOps practices and software delivery.
Description
AI DevOps — Build Smarter, Deploy Faster
Welcome to the next evolution of DevOps — where Artificial Intelligence meets Cloud Engineering, Automation, and Software Delivery.
This hands-on course takes you from traditional DevOps practices to AI-powered DevOps workflows, helping you understand how Generative AI, Large Language Models (LLMs), Agentic AI, and AI automation are transforming the way modern engineering teams build, deploy, and operate applications.
Throughout this course, you will learn how to leverage AI technologies with real-world DevOps tools and platforms to create smarter, faster, and more efficient engineering workflows.
What You Will Learn:
AI & Generative AI Fundamentals
Understand Artificial Intelligence, Machine Learning, Deep Learning, NLP, and Generative AI concepts
Learn how Large Language Models (LLMs) work
Explore LLM training phases: Pre-training, Supervised Fine-Tuning, and Reinforcement Learning
Prompt Engineering
Master the fundamentals of effective prompting
Understand prompt anatomy and different prompting techniques
Work with real-world prompt examples and AI models
Azure AI & Generative AI Services
Explore the Azure AI ecosystem
Understand Azure AI Foundry and its capabilities
Work with AI model evaluation, responsible AI, and guardrails
Learn Azure AI Search and Retrieval-Augmented Generation (RAG)
RAG (Retrieval-Augmented Generation) Applications
Understand RAG architecture and workflow
Learn how LLMs connect with enterprise knowledge sources
Build practical RAG solutions using Azure AI Search and AI models
Local LLMs with Hugging Face and Ollama
Explore the open-source AI ecosystem
Run Large Language Models locally using Ollama
Integrate local AI models with applications using APIs and Python
Agentic AI and AI Agents
Understand the evolution from Generative AI to Agentic AI
Learn AI agent architecture, workflows, and characteristics
Build intelligent multi-agent workflows using CrewAI
Create AI-powered DevOps automation scenarios
Model Context Protocol (MCP)
Understand how MCP enables AI agents to communicate with external tools
Learn MCP architecture, workflow, and components
Build and integrate MCP servers
Explore GitHub MCP, Azure DevOps MCP, and Terraform MCP use cases
AI-Powered DevOps Automation
Integrate AI with modern DevOps workflows
Automate infrastructure operations using Terraform MCP
Enhance software development workflows using GitHub MCP
Automate Azure DevOps activities using AI agents
Hands-On Projects Included:
- Build AI-powered workflows using Azure AI Foundry
- Create RAG applications with Azure AI Search
- Run local LLMs using Ollama
- Build AI agents using CrewAI
- Generate Kubernetes manifests using AI agents
- Automate infrastructure workflows using Terraform MCP
- Integrate AI agents with GitHub and Azure DevOps using MCP
By the end of this course, you will have the knowledge and practical skills to design and implement AI-driven DevOps solutions that improve automation, productivity, and operational intelligence.
Join the journey from DevOps to AI DevOps — Build Smarter, Deploy Faster.
Who this course is for:
- DevOps Engineers who want to integrate AI, LLMs, and Agentic AI into their daily workflows.
- Cloud Engineers and Platform Engineers looking to automate infrastructure, deployments, and operations using AI.
- Site Reliability Engineers (SREs) interested in AI-powered observability, incident analysis, and automated remediation.
- Azure DevOps, GitHub, and Terraform professionals who want to leverage Model Context Protocol (MCP) for intelligent automation.
- Software Developers who want to use AI to improve productivity, generate code, troubleshoot issues, and streamline DevOps processes.
- Solution Architects and Cloud Architects exploring AI-native software delivery and intelligent infrastructure management.
- IT Professionals and System Administrators looking to upskill in Generative AI and modern DevOps practices.
- Students, beginners, and technology enthusiasts who want to learn how Artificial Intelligence is transforming DevOps, even with little or no prior AI experience.
- Anyone preparing for the next generation of AI-powered DevOps roles and looking to stay ahead in the rapidly evolving technology landscape.