Follow these steps to begin your AZD learning journey:
- Fork the Repository: Click
- Clone the Repository:
git clone https://github.com/microsoft/azd-for-beginners.git - Join the Community: Azure Discord Communities for expert support
- Choose Your Learning Path: Select a chapter below that matches your experience level
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Master Azure Developer CLI (azd) through structured chapters designed for progressive learning. Special focus on AI application deployment with Azure AI Foundry integration.
Based on Azure AI Foundry Discord community insights, 45% of developers want to use AZD for AI workloads but encounter challenges with:
- Complex multi-service AI architectures
- Production AI deployment best practices
- Azure AI service integration and configuration
- Cost optimization for AI workloads
- Troubleshooting AI-specific deployment issues
By completing this structured course, you will:
- Master AZD Fundamentals: Core concepts, installation, and configuration
- Deploy AI Applications: Use AZD with Azure AI Foundry services
- Implement Infrastructure as Code: Manage Azure resources with Bicep templates
- Troubleshoot Deployments: Resolve common issues and debug problems
- Optimize for Production: Security, scaling, monitoring, and cost management
- Build Multi-Agent Solutions: Deploy complex AI architectures
Select your learning path based on experience level and goals
Prerequisites: Azure subscription, basic command line knowledge
Duration: 30-45 minutes
Complexity: ⭐
- Understanding Azure Developer CLI fundamentals
- Installing AZD on your platform
- Your first successful deployment
- 🎯 Start Here: What is Azure Developer CLI?
- 📖 Theory: AZD Basics - Core concepts and terminology
- ⚙️ Setup: Installation & Setup - Platform-specific guides
- 🛠️ Hands-On: Your First Project - Step-by-step tutorial
- 📋 Quick Reference: Command Cheat Sheet
# Quick installation check
azd version
# Deploy your first application
azd init --template todo-nodejs-mongo
azd up💡 Chapter Outcome: Successfully deploy a simple web application to Azure using AZD
Prerequisites: Chapter 1 completed
Duration: 1-2 hours
Complexity: ⭐⭐
- Azure AI Foundry integration with AZD
- Deploying AI-powered applications
- Understanding AI service configurations
- 🎯 Start Here: Azure AI Foundry Integration
- 📖 Patterns: AI Model Deployment - Deploy and manage AI models
- 🛠️ Workshop: AI Workshop Lab - Make your AI solutions AZD-ready
- 🎥 Interactive Guide: Workshop Materials - Browser-based learning with MkDocs * DevContainer Environment
- 📋 Templates: Azure AI Foundry Templates
# Deploy your first AI application
azd init --template azure-search-openai-demo
azd up
# Try additional AI templates
azd init --template openai-chat-app-quickstart
azd init --template agent-openai-python-prompty💡 Chapter Outcome: Deploy and configure an AI-powered chat application with RAG capabilities
Prerequisites: Chapter 1 completed
Duration: 45-60 minutes
Complexity: ⭐⭐
- Environment configuration and management
- Authentication and security best practices
- Resource naming and organization
- 📖 Configuration: Configuration Guide - Environment setup
- 🔐 Security: Authentication patterns and managed identity
- 📝 Examples: Database App Example - Configuration patterns
- Configure multiple environments (dev, staging, prod)
- Set up managed identity authentication
- Implement environment-specific configurations
💡 Chapter Outcome: Manage multiple environments with proper authentication and security
Prerequisites: Chapters 1-3 completed
Duration: 1-1.5 hours
Complexity: ⭐⭐⭐
- Advanced deployment patterns
- Infrastructure as Code with Bicep
- Resource provisioning strategies
- 📖 Deployment: Deployment Guide - Complete workflows
- 🏗️ Provisioning: Provisioning Resources - Azure resource management
- 📝 Examples: Container App Example - Containerized deployments
- Create custom Bicep templates
- Deploy multi-service applications
- Implement blue-green deployment strategies
💡 Chapter Outcome: Deploy complex multi-service applications using custom infrastructure templates
Prerequisites: Chapters 1-2 completed
Duration: 2-3 hours
Complexity: ⭐⭐⭐⭐
- Multi-agent architecture patterns
- Agent orchestration and coordination
- Production-ready AI deployments
- 🤖 Featured Project: Retail Multi-Agent Solution - Complete implementation
- 🛠️ ARM Templates: ARM Template Package - One-click deployment
- 📖 Architecture: Multi-agent coordination patterns
# Deploy the complete retail multi-agent solution
cd examples/retail-multiagent-arm-template
./deploy.sh
# Explore agent configurations
az deployment group show --resource-group <rg-name> --name <deployment-name>💡 Chapter Outcome: Deploy and manage a production-ready multi-agent AI solution with Customer and Inventory agents
Prerequisites: Chapter 4 completed
Duration: 1 hour
Complexity: ⭐⭐
- Capacity planning and resource validation
- SKU selection strategies
- Pre-flight checks and automation
- 📊 Planning: Capacity Planning - Resource validation
- 💰 Selection: SKU Selection - Cost-effective choices
- ✅ Validation: Pre-flight Checks - Automated scripts
- Run capacity validation scripts
- Optimize SKU selections for cost
- Implement automated pre-deployment checks
💡 Chapter Outcome: Validate and optimize deployments before execution
Prerequisites: Any deployment chapter completed
Duration: 1-1.5 hours
Complexity: ⭐⭐
- Systematic debugging approaches
- Common issues and solutions
- AI-specific troubleshooting
- 🔧 Common Issues: Common Issues - FAQ and solutions
- 🕵️ Debugging: Debugging Guide - Step-by-step strategies
- 🤖 AI Issues: AI-Specific Troubleshooting - AI service problems
- Diagnose deployment failures
- Resolve authentication issues
- Debug AI service connectivity
💡 Chapter Outcome: Independently diagnose and resolve common deployment issues
Prerequisites: Chapters 1-4 completed
Duration: 2-3 hours
Complexity: ⭐⭐⭐⭐
- Production deployment strategies
- Enterprise security patterns
- Monitoring and cost optimization
- 🏭 Production: Production AI Best Practices - Enterprise patterns
- 📝 Examples: Microservices Example - Complex architectures
- 📊 Monitoring: Application Insights integration
- Implement enterprise security patterns
- Set up comprehensive monitoring
- Deploy to production with proper governance
💡 Chapter Outcome: Deploy enterprise-ready applications with full production capabilities
Comprehensive hands-on learning with browser-based tools and guided exercises
Our workshop materials provide a structured, interactive learning experience that complements the chapter-based curriculum above. The workshop is designed for both self-paced learning and instructor-led sessions.
- Browser-Based Interface: Complete MkDocs-powered workshop with search, copy, and theme features
- GitHub Codespaces Integration: One-click development environment setup
- Structured Learning Path: 7-step guided exercises (3.5 hours total)
- Discovery → Deployment → Customization: Progressive methodology
- Interactive DevContainer Environment: Pre-configured tools and dependencies
The workshop follows a Discovery → Deployment → Customization methodology:
-
Discovery Phase (45 mins)
- Explore Azure AI Foundry templates and services
- Understand multi-agent architecture patterns
- Review deployment requirements and prerequisites
-
Deployment Phase (2 hours)
- Hands-on deployment of AI applications with AZD
- Configure Azure AI services and endpoints
- Implement security and authentication patterns
-
Customization Phase (45 mins)
- Modify applications for specific use cases
- Optimize for production deployment
- Implement monitoring and cost management
# Option 1: GitHub Codespaces (Recommended)
# Click "Code" → "Create codespace on main" in the repository
# Option 2: Local Development
git clone https://github.com/microsoft/azd-for-beginners.git
cd azd-for-beginners/workshop
# Follow the setup instructions in workshop/README.mdBy completing the workshop, participants will:
- Deploy Production AI Applications: Use AZD with Azure AI Foundry services
- Master Multi-Agent Architectures: Implement coordinated AI agent solutions
- Implement Security Best Practices: Configure authentication and access control
- Optimize for Scale: Design cost-effective, performant deployments
- Troubleshoot Deployments: Resolve common issues independently
- 🎥 Interactive Guide: Workshop Materials - Browser-based learning environment
- 📋 Step-by-Step Instructions: Guided Exercises - Detailed walkthroughs
- 🛠️ AI Workshop Lab: AI Workshop Lab - AI-focused exercises
- 💡 Quick Start: Workshop Setup Guide - Environment configuration
Perfect for: Corporate training, university courses, self-paced learning, and developer bootcamps.
Azure Developer CLI (azd) is a developer-centric command-line interface that accelerates the process of building and deploying applications to Azure. It provides:
- Template-based deployments - Use pre-built templates for common application patterns
- Infrastructure as Code - Manage Azure resources using Bicep or Terraform
- Integrated workflows - Seamlessly provision, deploy, and monitor applications
- Developer-friendly - Optimized for developer productivity and experience
Why AZD for AI Solutions? AZD addresses the top challenges AI developers face:
- AI-Ready Templates - Pre-configured templates for Azure OpenAI, Cognitive Services, and ML workloads
- Secure AI Deployments - Built-in security patterns for AI services, API keys, and model endpoints
- Production AI Patterns - Best practices for scalable, cost-effective AI application deployments
- End-to-End AI Workflows - From model development to production deployment with proper monitoring
- Cost Optimization - Smart resource allocation and scaling strategies for AI workloads
- Azure AI Foundry Integration - Seamless connection to AI Foundry model catalog and endpoints
Start here if you're deploying AI applications!
| Template | Chapter | Complexity | Services |
|---|---|---|---|
| Get started with AI chat | Chapter 2 | ⭐⭐ | AzureOpenAI + Azure AI Model Inference API + Azure AI Search + Azure Container Apps + Application Insights |
| Get started with AI agents | Chapter 2 | ⭐⭐ | Azure AI Agent Service + AzureOpenAI + Azure AI Search + Azure Container Apps + Application Insights |
| Multi-agent workflow automation | Chapter 5 | ⭐⭐⭐ | AzureOpenAI + Azure AI Agent Service + Semantic Kernel + Azure CosmosDB + Azure Container Apps |
| Generate documents from your data | Chapter 4 | ⭐⭐⭐ | AzureOpenAI + Azure AI Search + Azure AI Services + Azure CosmosDB |
| Improve client meetings with agents | Chapter 5 | ⭐⭐⭐ | AzureOpenAI + Azure AI Search + Azure CosmosDB + Azure SQL Database |
| Modernize your code with agents | Chapter 5 | ⭐⭐⭐ | AzureOpenAI + Azure Agent Service + Semantic Kernel + Azure CosmosDB + Azure Container Apps |
| Build your conversational agent | Chapter 4 | ⭐⭐⭐ | AI Language + AzureOpenAI + AI Search + Azure Storage + Azure Container Registry |
| Unlock insights from conversational data | Chapter 8 | ⭐⭐⭐ | AzureOpenAI + AI Search + Semantic Kernel + Azure Agent Service + AI AI Content Understanding |
| Multi-modal content processing | Chapter 8 | ⭐⭐⭐⭐ | AzureOpenAI + Azure Content Understanding + Azure CosmosDB + Azure Container Apps |
Production-ready application templates mapped to learning chapters
| Template | Learning Chapter | Complexity | Key Learning |
|---|---|---|---|
| openai-chat-app-quickstart | Chapter 2 | ⭐ | Basic AI deployment patterns |
| azure-search-openai-demo | Chapter 2 | ⭐⭐ | RAG implementation with Azure AI Search |
| ai-document-processing | Chapter 4 | ⭐⭐ | Document Intelligence integration |
| agent-openai-python-prompty | Chapter 5 | ⭐⭐⭐ | Agent framework and function calling |
| contoso-chat | Chapter 8 | ⭐⭐⭐ | Enterprise AI orchestration |
| retail-multi-agent-solution | Chapter 5 | ⭐⭐⭐⭐ | Multi-agent architecture with Customer and Inventory agents |
- Simple Web App - Basic deployment patterns
- Static Website - Static content deployment
- Basic API - REST API deployment
- Database App - Database connectivity patterns
- Data Processing - ETL workflow deployment
- Container Apps - Containerized deployments
- Microservices - Multi-service architectures
- Enterprise Solutions - Production-ready patterns
- Azure-Samples AZD Templates - Official Microsoft samples
- Awesome AZD Gallery - Community-contributed templates
- Examples Directory - Local learning examples with detailed explanations
- Command Cheat Sheet - Essential azd commands organized by chapter
- Glossary - Azure and azd terminology
- FAQ - Common questions organized by learning chapter
- Study Guide - Comprehensive practice exercises
- AI Workshop Lab - Make your AI solutions AZD-deployable (2-3 hours)
- Interactive Workshop Guide - Browser-based workshop with MkDocs and DevContainer Environment
- Structured Learning Path -7-step guided exercises (Discovery → Deployment → Customization)
- AZD For Beginners Workshop - Complete hands-on workshop materials with GitHub Codespaces integration
Track your learning progress through each chapter:
- Chapter 1: Foundation & Quick Start ✅
- Chapter 2: AI-First Development ✅
- Chapter 3: Configuration & Authentication ✅
- Chapter 4: Infrastructure as Code & Deployment ✅
- Chapter 5: Multi-Agent AI Solutions ✅
- Chapter 6: Pre-Deployment Validation & Planning ✅
- Chapter 7: Troubleshooting & Debugging ✅
- Chapter 8: Production & Enterprise Patterns ✅
After completing each chapter, verify your knowledge by:
- Practical Exercise: Complete the chapter's hands-on deployment
- Knowledge Check: Review the FAQ section for your chapter
- Community Discussion: Share your experience in Azure Discord
- Next Chapter: Move to the next complexity level
Upon completing all chapters, you will have:
- Production Experience: Deployed real AI applications to Azure
- Professional Skills: Enterprise-ready deployment capabilities
- Community Recognition: Active member of Azure developer community
- Career Advancement: In-demand AZD and AI deployment expertise
- Technical Issues: Report bugs and request features
- Learning Questions: Microsoft Azure Discord Community
- AI-Specific Help: Join the #Azure channel for AZD + AI Foundry discussions
- Documentation: Official Azure Developer CLI documentation
Recent Poll Results from #Azure Channel:
- 45% of developers want to use AZD for AI workloads
- Top challenges: Multi-service deployments, credential management, production readiness
- Most requested: AI-specific templates, troubleshooting guides, best practices
Join our community to:
- Share your AZD + AI experiences and get help
- Access early previews of new AI templates
- Contribute to AI deployment best practices
- Influence future AI + AZD feature development
We welcome contributions! Please read our Contributing Guide for details on:
- Content Improvements: Enhance existing chapters and examples
- New Examples: Add real-world scenarios and templates
- Translation: Help maintain multi-language support
- Bug Reports: Improve accuracy and clarity
- Community Standards: Follow our inclusive community guidelines
This project is licensed under the MIT License - see the LICENSE file for details.
Our team produces other comprehensive learning courses:
- Model Context Protocol (MCP) For Beginners
- AI Agents for Beginners
- Generative AI for Beginners using .NET
- Generative AI for Beginners
- Generative AI for Beginners using Java
- ML for Beginners
- Data Science for Beginners
- AI for Beginners
- Cybersecurity for Beginners
- Web Dev for Beginners
- IoT for Beginners
- XR Development for Beginners
- Mastering GitHub Copilot for AI Paired Programming
- Mastering GitHub Copilot for C#/.NET Developers
- Choose Your Own Copilot Adventure
🚀 Ready to Start Learning?
Beginners: Start with Chapter 1: Foundation & Quick Start
AI Developers: Jump to Chapter 2: AI-First Development
Experienced Developers: Begin with Chapter 3: Configuration & Authentication
Next Steps: Begin Chapter 1 - AZD Basics →