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
This course is designed to support both self-paced individual learning and facilitated workshop sessions, enabling learners to get hands-on experience with AZD while developing practical skills through interactive exercises.
Perfect for individual developers and continuous learning
Features:
- Browser-Based Interface: Complete MkDocs-powered workshop accessible through any web browser
- GitHub Codespaces Integration: One-click development environment with pre-configured tools
- Interactive DevContainer Environment: No local setup required - start coding immediately
- Progress Tracking: Built-in checkpoints and validation exercises
- Community Support: Access to Azure Discord channels for questions and collaboration
Learning Structure:
- Flexible Timing: Complete chapters at your own pace over days or weeks
- Checkpoint System: Validate learning before advancing to complex topics
- Resource Library: Comprehensive documentation, examples, and troubleshooting guides
- Portfolio Development: Build deployable projects for professional portfolios
Getting Started (Self-Paced):
# Option 1: GitHub Codespaces (Recommended)
# Navigate to the repository and click "Code" → "Create codespace on main"
# Option 2: Local Development
git clone https://github.com/microsoft/azd-for-beginners.git
cd azd-for-beginners/workshop
# Follow setup instructions in workshop/README.mdIdeal for corporate training, bootcamps, and educational institutions
Workshop Format Options:
📚 Academic Course Integration (8-12 weeks)
- University Programs: Semester-long course with weekly 2-hour sessions
- Bootcamp Format: Intensive 3-5 day program with daily 6-8 hour sessions
- Corporate Training: Monthly team sessions with practical project implementation
- Assessment Framework: Graded assignments, peer reviews, and final projects
🚀 Intensive Workshop (1-3 days)
- Day 1: Foundation + AI Development (Chapters 1-2) - 6 hours
- Day 2: Configuration + Infrastructure (Chapters 3-4) - 6 hours
- Day 3: Advanced Patterns + Production (Chapters 5-8) - 8 hours
- Follow-up: Optional 2-week mentorship for project completion
⚡ Executive Briefing (4-6 hours)
- Strategic Overview: AZD value proposition and business impact (1 hour)
- Hands-On Demo: Deploy AI application end-to-end (2 hours)
- Architecture Review: Enterprise patterns and governance (1 hour)
- Implementation Planning: Organizational adoption strategy (1-2 hours)
Discovery → Deployment → Customization approach for hands-on skill development
Phase 1: Discovery (45 minutes)
- Template Exploration: Evaluate Microsoft Foundry templates and services
- Architecture Analysis: Understand multi-agent patterns and deployment strategies
- Requirement Assessment: Identify organizational needs and constraints
- Environment Setup: Configure development environment and Azure resources
Phase 2: Deployment (2 hours)
- Guided Implementation: Step-by-step deployment of AI applications with AZD
- Service Configuration: Configure Azure AI services, endpoints, and authentication
- Security Implementation: Apply enterprise security patterns and access controls
- Validation Testing: Verify deployments and troubleshoot common issues
Phase 3: Customization (45 minutes)
- Application Modification: Adapt templates for specific use cases and requirements
- Production Optimization: Implement monitoring, cost management, and scaling strategies
- Advanced Patterns: Explore multi-agent coordination and complex architectures
- Next Steps Planning: Define learning path for continued skill development
Measurable skills developed through hands-on practice
Technical Competencies:
- Deploy Production AI Applications: Successfully deploy and configure AI-powered solutions
- Infrastructure as Code Mastery: Create and manage custom Bicep templates
- Multi-Agent Architecture: Implement coordinated AI agent solutions
- Production Readiness: Apply security, monitoring, and governance patterns
- Troubleshooting Expertise: Independently resolve deployment and configuration issues
Professional Skills:
- Project Leadership: Lead technical teams in cloud deployment initiatives
- Architecture Design: Design scalable, cost-effective Azure solutions
- Knowledge Transfer: Train and mentor colleagues in AZD best practices
- Strategic Planning: Influence organizational cloud adoption strategies
Comprehensive toolkit for facilitators and learners
For Facilitators:
- Instructor Guide: Workshop Overview - Session planning and delivery guidance
- Presentation Materials: Slide decks, architecture diagrams, and demo scripts
- Assessment Tools: Practical exercises, knowledge checks, and evaluation rubrics
- Technical Setup: Environment configuration, troubleshooting guides, and backup plans
For Learners:
- Interactive Workshop Environment: Workshop Materials - Browser-based learning platform
- Step-by-Step Instructions: Guided Exercises - Detailed implementation walkthroughs
- Reference Documentation: AI Workshop Lab - AI-focused deep dives
- Community Resources: Azure Discord channels, GitHub discussions, and expert support
Organizational deployment and training strategies
Corporate Training Programs:
- Developer Onboarding: New hire orientation with AZD fundamentals (2-4 weeks)
- Team Upskilling: Quarterly workshops for existing development teams (1-2 days)
- Architecture Review: Monthly sessions for senior engineers and architects (4 hours)
- Leadership Briefings: Executive workshops for technical decision makers (half-day)
Implementation Support:
- Custom Workshop Design: Tailored content for specific organizational needs
- Pilot Program Management: Structured rollout with success metrics and feedback loops
- Ongoing Mentorship: Post-workshop support for project implementation
- Community Building: Internal Azure AI developer communities and knowledge sharing
Success Metrics:
- Skill Acquisition: Pre/post assessments measuring technical competency growth
- Deployment Success: Percentage of participants successfully deploying production applications
- Time to Productivity: Reduced onboarding time for new Azure AI projects
- Knowledge Retention: Follow-up assessments 3-6 months post-workshop
Prerequisites: Azure subscription, basic command line knowledge
Complexity: ⭐
- Understanding Azure Developer CLI fundamentals
- Installing AZD on your platform
- Your first successful deployment
- Core concepts and terminology
- AZD Basics - Core concepts
- Installation & Setup - Platform-specific guides
- Your First Project - Hands-on tutorial
- Command Cheat Sheet - Quick reference
Successfully deploy a simple web application to Azure using AZD
Prerequisites: Chapter 1 completed
Complexity: ⭐⭐
- Microsoft Foundry integration with AZD
- Deploying AI-powered applications
- Understanding AI service configurations
- RAG (Retrieval-Augmented Generation) patterns
- Microsoft Foundry Integration
- AI Model Deployment
- AI Workshop Lab - NEW: Comprehensive 2-3 hour hands-on lab
- Interactive Workshop Guide - NEW: Browser-based workshop with MkDocs preview
- Microsoft Foundry Templates
- Workshop Instructions - NEW: Step-by-step guided exercises
Deploy and configure an AI-powered chat application with RAG capabilities
NEW Interactive Experience: Complete Workshop Guide
- Discovery (30 mins): Template selection and evaluation
- Deployment (45 mins): Deploy and validate AI template functionality
- Deconstruction (30 mins): Understand template architecture and components
- Configuration (30 mins): Customize settings and parameters
- Customization (45 mins): Modify and iterate to make it yours
- Teardown (15 mins): Clean up resources and understand lifecycle
- Wrap-up (15 mins): Next steps and advanced learning paths
Prerequisites: Chapter 1 completed
Complexity: ⭐⭐
- Environment configuration and management
- Authentication and security best practices
- Resource naming and organization
- Multi-environment deployments
- Configuration Guide - Environment setup
- Authentication & Security Patterns - Managed identity and Key Vault integration
- Multi-environment examples
Manage multiple environments with proper authentication and security
Prerequisites: Chapters 1-3 completed
Complexity: ⭐⭐⭐
-
Advanced deployment patterns
-
Infrastructure as Code with Bicep
-
Resource provisioning strategies
-
Custom template creation
-
Containerized application deployment with Azure Container Apps and AZD
- Deployment Guide - Complete workflows
- Provisioning Resources - Resource management
- Container and microservices examples
- Container App Examples - Quick start, production, and advanced deployment patterns
Deploy complex multi-service applications using custom infrastructure templates
Prerequisites: Chapters 1-2 completed
Complexity: ⭐⭐⭐⭐
-
Multi-agent architecture patterns
-
Agent orchestration and coordination
-
Production-ready AI deployments
-
Customer and Inventory agent implementations
-
Integrating containerized microservices as part of agent-based solutions
- Retail Multi-Agent Solution - Complete implementation
- ARM Template Package - One-click deployment
- Multi-agent coordination patterns
- Microservices Architecture Example - Service-to-service communication, async messaging, and production deployment
Deploy and manage a production-ready multi-agent AI solution
Prerequisites: Chapter 4 completed
Complexity: ⭐⭐
- Capacity planning and resource validation
- SKU selection strategies
- Pre-flight checks and automation
- Cost optimization planning
- Capacity Planning - Resource validation
- SKU Selection - Cost-effective choices
- Pre-flight Checks - Automated scripts
- Application Insights Integration - Monitoring and observability
- Multi-Agent Coordination Patterns - Agent orchestration strategies
Validate and optimize deployments before execution
Prerequisites: Any deployment chapter completed
Complexity: ⭐⭐
- Systematic debugging approaches
- Common issues and solutions
- AI-specific troubleshooting
- Performance optimization
- Common Issues - FAQ and solutions
- Debugging Guide - Step-by-step strategies
- AI-Specific Troubleshooting - AI service problems
Independently diagnose and resolve common deployment issues
Prerequisites: Chapters 1-4 completed
Complexity: ⭐⭐⭐⭐
-
Production deployment strategies
-
Enterprise security patterns
-
Monitoring and cost optimization
-
Scalability and governance
-
Best practices for production container app deployments (security, monitoring, cost, CI/CD)
- Production AI Best Practices - Enterprise patterns
- Microservices and enterprise examples
- Monitoring and governance frameworks
- Microservices Architecture Example - Blue-green/canary deployment, distributed tracing, and cost optimization
Deploy enterprise-ready applications with full production capabilities
-
🌱 Beginners: Start with Chapter 1 (Foundation) → Chapter 2 (AI Development)
-
🔧 Intermediate: Chapters 3-4 (Configuration & Infrastructure) → Chapter 6 (Validation)
-
🚀 Advanced: Chapter 5 (Multi-Agent Solutions) → Chapter 7 (Troubleshooting)
-
🏢 Enterprise: Complete all chapters, focus on Chapter 8 (Production Patterns)
-
Container App Path: Chapters 4 (Containerized deployment), 5 (Microservices integration), 8 (Production best practices)
- ⭐ Basic: Single concepts, guided tutorials, 30-60 minutes
- ⭐⭐ Intermediate: Multiple concepts, hands-on practice, 1-2 hours
- ⭐⭐⭐ Advanced: Complex architectures, custom solutions, 1-3 hours
- ⭐⭐⭐⭐ Expert: Production systems, enterprise patterns, 2-4 hours
- Chapter 1: Foundation & Quick Start (45 mins)
- Chapter 2: AI-First Development (2 hours)
- Chapter 5: Multi-Agent AI Solutions (3 hours)
- Chapter 8: Production AI Best Practices (1 hour)
- Chapter 1: Foundation & Quick Start (45 mins)
- Chapter 3: Configuration & Authentication (1 hour)
- Chapter 4: Infrastructure as Code & Deployment (1.5 hours)
- Chapter 6: Pre-Deployment Validation & Planning (1 hour)
- Chapter 7: Troubleshooting & Debugging (1.5 hours)
- Chapter 8: Production & Enterprise Patterns (2 hours)
Sequential completion of all 8 chapters with hands-on practice and validation
- Chapter Checkpoints: Practical exercises with measurable outcomes
- Hands-On Verification: Deploy working solutions for each chapter
- Progress Tracking: Visual indicators and completion badges
- Community Validation: Share experiences in Azure Discord channels
- ✅ Deploy basic web application using AZD
- ✅ Deploy AI-powered chat application with RAG
- ✅ Understand AZD core concepts and AI integration
- ✅ Manage multi-environment deployments
- ✅ Create custom Bicep infrastructure templates
- ✅ Implement secure authentication patterns
- ✅ Deploy complex multi-agent AI solution
- ✅ Perform capacity planning and cost optimization
- ✅ Implement automated pre-deployment validation
- ✅ Debug and resolve deployment issues independently
- ✅ Implement enterprise-grade monitoring and security
- ✅ Deploy production-ready applications with governance
- Course Completion Badge: Complete all 8 chapters with practical validation
- Community Recognition: Active participation in Microsoft Foundry Discord
- Professional Development: Industry-relevant AZD and AI deployment skills
- Career Advancement: Enterprise-ready cloud deployment capabilities
Upon completion of foundation chapters, learners will demonstrate:
Technical Capabilities:
- Deploy simple web applications to Azure using AZD commands
- Configure and deploy AI-powered chat applications with RAG capabilities
- Understand core AZD concepts: templates, environments, provisioning workflows
- Integrate Microsoft Foundry services with AZD deployments
- Navigate Azure AI service configurations and API endpoints
Professional Skills:
- Follow structured deployment workflows for consistent results
- Troubleshoot basic deployment issues using logs and documentation
- Communicate effectively about cloud deployment processes
- Apply best practices for secure AI service integration
Learning Verification:
- ✅ Successfully deploy
todo-nodejs-mongotemplate - ✅ Deploy and configure
azure-search-openai-demowith RAG - ✅ Complete interactive workshop exercises (Discovery phase)
- ✅ Participate in Azure Discord community discussions
Upon completion of intermediate chapters, learners will demonstrate:
Technical Capabilities:
- Manage multi-environment deployments (dev, staging, production)
- Create custom Bicep templates for infrastructure as code
- Implement secure authentication patterns with managed identity
- Deploy complex multi-service applications with custom configurations
- Optimize resource provisioning strategies for cost and performance
Professional Skills:
- Design scalable infrastructure architectures
- Implement security best practices for cloud deployments
- Document infrastructure patterns for team collaboration
- Evaluate and select appropriate Azure services for requirements
Learning Verification:
- ✅ Configure separate environments with environment-specific settings
- ✅ Create and deploy custom Bicep template for multi-service application
- ✅ Implement managed identity authentication for secure access
- ✅ Complete configuration management exercises with real scenarios
Upon completion of advanced chapters, learners will demonstrate:
Technical Capabilities:
- Deploy and orchestrate multi-agent AI solutions with coordinated workflows
- Implement Customer and Inventory agent architectures for retail scenarios
- Perform comprehensive capacity planning and resource validation
- Execute automated pre-deployment validation and optimization
- Design cost-effective SKU selections based on workload requirements
Professional Skills:
- Architect complex AI solutions for production environments
- Lead technical discussions about AI deployment strategies
- Mentor junior developers in AZD and AI deployment best practices
- Evaluate and recommend AI architecture patterns for business requirements
Learning Verification:
- ✅ Deploy complete retail multi-agent solution with ARM templates
- ✅ Demonstrate agent coordination and workflow orchestration
- ✅ Complete capacity planning exercises with real resource constraints
- ✅ Validate deployment readiness through automated pre-flight checks
Upon completion of expert chapters, learners will demonstrate:
Technical Capabilities:
- Diagnose and resolve complex deployment issues independently
- Implement enterprise-grade security patterns and governance frameworks
- Design comprehensive monitoring and alerting strategies
- Optimize production deployments for scale, cost, and performance
- Establish CI/CD pipelines with proper testing and validation
Professional Skills:
- Lead enterprise cloud transformation initiatives
- Design and implement organizational deployment standards
- Train and mentor development teams in advanced AZD practices
- Influence technical decision-making for enterprise AI deployments
Learning Verification:
- ✅ Resolve complex multi-service deployment failures
- ✅ Implement enterprise security patterns with compliance requirements
- ✅ Design and deploy production monitoring with Application Insights
- ✅ Complete enterprise governance framework implementation
Track your learning progress through structured checkpoints:
- 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 through:
- Practical Exercise Completion: Deploy working solutions for each chapter
- Knowledge Assessment: Review FAQ sections and complete self-assessments
- Community Engagement: Share experiences and get feedback in Azure Discord
- Portfolio Development: Document your deployments and lessons learned
- Peer Review: Collaborate with other learners on complex scenarios
Upon completing all chapters with verification, graduates will have:
Technical Expertise:
- Production Experience: Deployed real AI applications to Azure environments
- Professional Skills: Enterprise-ready deployment and troubleshooting capabilities
- Architecture Knowledge: Multi-agent AI solutions and complex infrastructure patterns
- Troubleshooting Mastery: Independent resolution of deployment and configuration issues
Professional Development:
- Industry Recognition: Verifiable skills in high-demand AZD and AI deployment areas
- Career Advancement: Qualifications for cloud architect and AI deployment specialist roles
- Community Leadership: Active membership in Azure developer and AI communities
- Continuous Learning: Foundation for advanced Microsoft Foundry specialization
Portfolio Assets:
- Deployed Solutions: Working examples of AI applications and infrastructure patterns
- Documentation: Comprehensive deployment guides and troubleshooting procedures
- Community Contributions: Discussions, examples, and improvements shared with Azure community
- Professional Network: Connections with Azure experts and AI deployment practitioners
Graduates are prepared for advanced specialization in:
- Microsoft Foundry Expert: Deep specialization in AI model deployment and orchestration
- Cloud Architecture Leadership: Enterprise-scale deployment design and governance
- Developer Community Leadership: Contributing to Azure samples and community resources
- Corporate Training: Teaching AZD and AI deployment skills within organizations