Follow these steps to begin your AZD learning journey:

1. Fork the Repository: Click
2. Clone the Repository: git clone https://github.com/microsoft/azd-for-beginners.git
3. Join the Community: Azure Discord Communities for expert support
4. 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 Microsoft Foundry integration.

Based on Microsoft 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 Microsoft 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

✅ Success Validation:

# After completing Chapter 1, you should be able to:
azd version              # Shows installed version
azd init --template todo-nodejs-mongo  # Initializes project
azd up                  # Deploys to Azure
azd show                # Displays running app URL
# Application opens in browser and works
azd down --force --purge  # Cleans up resources

📊 Time Investment: 30-45 minutes
📈 Skill Level After: Can deploy basic applications independently

✅ Success Validation:

# After completing Chapter 1, you should be able to:
azd version              # Shows installed version
azd init --template todo-nodejs-mongo  # Initializes project
azd up                  # Deploys to Azure
azd show                # Displays running app URL
# Application opens in browser and works
azd down --force --purge  # Cleans up resources

📊 Time Investment: 30-45 minutes
📈 Skill Level After: Can deploy basic applications independently

Prerequisites: Chapter 1 completed
Duration: 1-2 hours
Complexity: ⭐⭐

• Microsoft Foundry integration with AZD
• Deploying AI-powered applications
• Understanding AI service configurations

• 🎯 Start Here: Microsoft 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: Microsoft Foundry Templates
• 📝 Examples: AZD Deployment Examples

# 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

✅ Success Validation:

# After Chapter 2, you should be able to:
azd init --template azure-search-openai-demo
azd up
# Test the AI chat interface
# Ask questions and get AI-powered responses with sources
# Verify search integration works
azd monitor  # Check Application Insights shows telemetry
azd down --force --purge

📊 Time Investment: 1-2 hours
📈 Skill Level After: Can deploy and configure production-ready AI applications
💰 Cost Awareness: Understand $80-150/month dev costs, $300-3500/month production costs

Development Environment (Estimated $80-150/month):

• Azure OpenAI (Pay-as-you-go): $0-50/month (based on token usage)
• AI Search (Basic tier): $75/month
• Container Apps (Consumption): $0-20/month
• Storage (Standard): $1-5/month

Production Environment (Estimated $300-3,500+/month):

• Azure OpenAI (PTU for consistent performance): $3,000+/month OR Pay-as-go with high volume
• AI Search (Standard tier): $250/month
• Container Apps (Dedicated): $50-100/month
• Application Insights: $5-50/month
• Storage (Premium): $10-50/month

💡 Cost Optimization Tips:

• Use Free Tier Azure OpenAI for learning (50,000 tokens/month included)
• Run azd down to deallocate resources when not actively developing
• Start with consumption-based billing, upgrade to PTU only for production
• Use azd provision --preview to estimate costs before deployment
• Enable auto-scaling: pay only for actual usage

Cost Monitoring:

# Check estimated monthly costs
azd provision --preview

# Monitor actual costs in Azure Portal
az consumption budget list --resource-group <your-rg>

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 - Authentication patterns
• 📝 Examples: Database App Example - AZD Database Examples

• 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 - 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 - Monitoring

• Implement enterprise security patterns
• Set up comprehensive monitoring
• Deploy to production with proper governance

💡 Chapter Outcome: Deploy enterprise-ready applications with full production capabilities

⚠️ WORKSHOP STATUS: Active Development
The workshop materials are currently being developed and refined. Core modules are functional, but some advanced sections are incomplete. We're actively working to complete all content. Track progress →

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:

1. Discovery Phase (45 mins)

   • Explore Microsoft Foundry templates and services
   • Understand multi-agent architecture patterns
   • Review deployment requirements and prerequisites

2. Deployment Phase (2 hours)

   • Hands-on deployment of AI applications with AZD
   • Configure Azure AI services and endpoints
   • Implement security and authentication patterns

3. 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.md

By completing the workshop, participants will:

• Deploy Production AI Applications: Use AZD with Microsoft 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
• Microsoft Foundry Integration - Seamless connection to Microsoft Foundry model catalog and endpoints

Start here if you're deploying AI applications!

Note: These templates demonstrate various AI patterns. Some are external Azure Samples, others are local implementations.

Production-ready application templates mapped to learning chapters

📌 Local vs. External Examples:
Local Examples (in this repo) = Ready to use immediately
External Examples (Azure Samples) = Clone from linked repositories

• Retail Multi-Agent Solution - Complete production-ready implementation with ARM templates
  • Multi-agent architecture (Customer + Inventory agents)
  • Comprehensive monitoring and evaluation
  • One-click deployment via ARM template

Comprehensive container deployment examples in this repository:

• Container App Examples - Complete guide to containerized deployments
  • Simple Flask API - Basic REST API with scale-to-zero
  • Microservices Architecture - Production-ready multi-service deployment
  • Quick Start, Production, and Advanced deployment patterns
  • Monitoring, security, and cost optimization guidance

Clone these Azure Samples repositories to get started:

• Simple Web App - Node.js + MongoDB - Basic deployment patterns
• Static Website - React SPA - Static content deployment
• Container App - Python Flask - REST API deployment

• Database App - C# + SQL - Database connectivity patterns
• Functions + Cosmos DB - Serverless data workflow

• Java Microservices - Multi-service architectures
• Container Apps Jobs - Background processing
• Enterprise ML Pipeline - Production-ready ML patterns

• Official AZD Template Gallery - Curated collection of official and community templates
• Azure Developer CLI Templates - Microsoft Learn template documentation
• 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

• Azure Developer CLI Documentation
• Azure Architecture Center
• Azure Pricing Calculator
• Azure Status

Common issues beginners face and immediate solutions:

# Install AZD first
# Windows (PowerShell):
winget install microsoft.azd

# macOS:
brew tap azure/azd && brew install azd

# Linux:
curl -fsSL https://aka.ms/install-azd.sh | bash

# Verify installation
azd version

# List available subscriptions
az account list --output table

# Set default subscription
az account set --subscription "<subscription-id-or-name>"

# Set for AZD environment
azd env set AZURE_SUBSCRIPTION_ID "<subscription-id>"

# Verify
az account show

# Try different Azure region
azd env set AZURE_LOCATION "westus2"
azd up

# Or use smaller SKUs in development
# Edit infra/main.parameters.json:
{
  "sku": "B1"  // Instead of "P1V2"
}

# Option 1: Clean and retry
azd down --force --purge
azd up

# Option 2: Just fix infrastructure
azd provision

# Option 3: Check detailed logs
azd show
azd logs

# Re-authenticate
az logout
az login

azd auth logout
azd auth login

# Verify authentication
az account show

# AZD generates unique names, but if conflict:
azd down --force --purge

# Then retry with fresh environment
azd env new dev-v2
azd up

Normal wait times:

• Simple web app: 5-10 minutes
• App with database: 10-15 minutes
• AI applications: 15-25 minutes (OpenAI provisioning is slow)

# Check progress
azd show

# If stuck >30 minutes, check Azure Portal:
azd monitor
# Look for failed deployments

# Check your Azure role
az role assignment list --assignee $(az account show --query user.name -o tsv)

# You need at least "Contributor" role
# Ask your Azure admin to grant:
# - Contributor (for resources)
# - User Access Administrator (for role assignments)

# Show all service endpoints
azd show

# Or open Azure Portal
azd monitor

# Check specific service
azd env get-values
# Look for *_URL variables

• Common Issues Guide: Detailed Solutions
• AI-Specific Issues: AI Troubleshooting
• Debugging Guide: Step-by-step Debugging
• Get Help: Azure Discord #azure-developer-cli

Common issues beginners face and immediate solutions:

# Install AZD first
# Windows (PowerShell):
winget install microsoft.azd

# macOS:
brew tap azure/azd && brew install azd

# Linux:
curl -fsSL https://aka.ms/install-azd.sh | bash

# Verify installation
azd version

# List available subscriptions
az account list --output table

# Set default subscription
az account set --subscription "<subscription-id-or-name>"

# Set for AZD environment
azd env set AZURE_SUBSCRIPTION_ID "<subscription-id>"

# Verify
az account show

# Try different Azure region
azd env set AZURE_LOCATION "westus2"
azd up

# Or use smaller SKUs in development
# Edit infra/main.parameters.json:
{
  "sku": "B1"  // Instead of "P1V2"
}

# Option 1: Clean and retry
azd down --force --purge
azd up

# Option 2: Just fix infrastructure
azd provision

# Option 3: Check detailed logs
azd show
azd logs

# Re-authenticate
az logout
az login

azd auth logout
azd auth login

# Verify authentication
az account show

# AZD generates unique names, but if conflict:
azd down --force --purge

# Then retry with fresh environment
azd env new dev-v2
azd up

# Check progress
azd show

# If stuck >30 minutes, check Azure Portal:
azd monitor
# Look for failed deployments

# Check your Azure role
az role assignment list --assignee $(az account show --query user.name -o tsv)

# You need at least "Contributor" role
# Ask your Azure admin to grant:
# - Contributor (for resources)
# - User Access Administrator (for role assignments)

# Show all service endpoints
azd show

# Or open Azure Portal
azd monitor

# Check specific service
azd env get-values
# Look for *_URL variables

• Common Issues Guide: Detailed Solutions
• AI-Specific Issues: AI Troubleshooting
• Debugging Guide: Step-by-step Debugging
• Get Help: Azure Discord #azure-developer-cli

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:

1. Practical Exercise: Complete the chapter's hands-on deployment
2. Knowledge Check: Review the FAQ section for your chapter
3. Community Discussion: Share your experience in Azure Discord
4. 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 and
• AI-Specific Help: Join the
• 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:

🚀 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 →