Microsoft Ignite 2025

🔥 AI-Ready Apps: Containerize and Modernize with Azure

Welcome to the Microsoft Ignite 2025 session PREL15: AI-Ready Apps - Containerize and Modernize with Azure!

🎯 Overview

Modernize containerized apps with AI on Azure. This hands-on lab demonstrates how to quickly deploy powerful, flexible AI-powered applications to Azure Container Apps. You'll gain hands-on experience using Azure OpenAI and open-source models on serverless GPUs for cost-efficient AI inferencing, while securing enterprise-grade apps and ensuring compliance.

What You'll Learn

• Azure Container Apps: Deploy containerized AI applications with serverless GPUs
• Azure OpenAI Integration: Build intelligent applications with GPT models using LangChain
• Open-Source AI Models: Run Ollama and local LLMs on GPU-enabled containers
• Dynamic Sessions: Execute untrusted code safely in isolated Python environments
• AI Agent Development: Build autonomous agents with MCP and Goose
• Cost-Efficient AI: Optimize AI inferencing with GPU-based compute
• Enterprise Security: Implement secure, compliant AI applications
• Modern Development Practices: Use Infrastructure as Code (IaC) and containerization

📚 Repository Structure

├── docs/           # Comprehensive documentation and MkDocs site
├── lab/            # Hands-on lab materials and instructions
│   ├── instructions/  # Step-by-step lab guides
│   └── README.md      # Lab overview and setup
├── src/            # Source code samples and templates
├── data/           # Sample data files
└── img/            # Images and diagrams

🚀 Getting Started

Prerequisites

• Azure subscription with appropriate permissions
• Azure CLI installed and configured
• WSL2 (Windows Subsystem for Linux) or Linux environment
• Python 3.12+
• VS Code or preferred IDE

Quick Start

1. Clone this repository

   git clone https://github.com/Azure-Samples/ignite25-PREL15-ai-ready-apps-containerize-and-modernize-with-azure.git
   cd ignite25-PREL15-ai-ready-apps-containerize-and-modernize-with-azure

2. Review the lab instructions

   • See lab/README.md for detailed setup and lab guide
   • Follow the step-by-step instructions in lab/instructions/

3. Set up your Azure environment

   • Ensure you have the required Azure resource providers registered
   • Configure your Azure CLI authentication

🧪 Lab Exercises

This lab consists of multiple segments covering:

1. AI & GPU Playbook: Understanding AI workloads and GPU acceleration on Azure
2. Environment Setup: Configure Azure resources and development environment
3. Azure OpenAI Deployment: Create and configure Azure OpenAI resources with GPT models
4. Ollama & Open-Source Models: Deploy local LLMs on serverless GPUs
5. Dynamic Sessions: Set up Azure Container Apps session pools for code execution
6. MCP Shell Integration: Implement Model Context Protocol for AI agents
7. Goose AI Agent: Build autonomous coding agents with Goose
8. LangChain Integration: Build AI-powered applications combining multiple AI services
9. Testing & Deployment: Test and deploy your containerized AI applications

See lab/README.md for complete lab instructions.

📖 Documentation

Comprehensive documentation is available in the /docs directory and can be viewed as a MkDocs site:

pip install -r requirements.txt
mkdocs serve

Then navigate to http://localhost:8000

🔑 Key Technologies

• Azure Container Apps: Serverless container platform with GPU support and dynamic sessions
• Azure OpenAI Service: Enterprise-grade AI models (GPT-3.5/GPT-4)
• Ollama: Run open-source LLMs (Llama, Mistral, Phi) locally and on Azure
• GPU Acceleration: Serverless GPU compute for cost-efficient AI inferencing
• LangChain: Framework for building LLM-powered applications
• MCP (Model Context Protocol): Connect AI agents to external tools and data
• Goose AI Agent: Autonomous coding agent for software development
• Python: Primary programming language
• FastAPI: Modern web framework for building APIs
• Docker: Containerization platform

📚 Learning Resources

Topic	Link
Azure Container Apps Overview	https://learn.microsoft.com/azure/container-apps/overview
Azure OpenAI Service Documentation	https://learn.microsoft.com/azure/ai-services/openai/overview
Azure OpenAI Quickstart	https://learn.microsoft.com/azure/ai-services/openai/quickstart
Ollama Documentation	https://ollama.com/
Azure Container Apps Dynamic Sessions	https://learn.microsoft.com/azure/container-apps/sessions
Code Interpreter in Dynamic Sessions	https://learn.microsoft.com/azure/container-apps/sessions-code-interpreter
LangChain Python Documentation	https://python.langchain.com/
LangChain Azure Integration	https://python.langchain.com/docs/integrations/platforms/microsoft
Model Context Protocol (MCP)	https://modelcontextprotocol.io/
Azure Container Apps Security	https://learn.microsoft.com/azure/container-apps/security
Azure Well-Architected Framework	https://learn.microsoft.com/azure/well-architected/
Learn at Ignite 2025	https://aka.ms/LearnAtIgnite
Ignite 2025 Next Steps	https://aka.ms/Ignite25-Next-Steps

📋 Additional Resources

• Azure Container Apps Documentation
• Azure Container Apps Dynamic Sessions
• Azure OpenAI Service
• LangChain Documentation
• Microsoft Ignite 2025
• Microsoft Learn At Ignite
• Ignite 2025 Next Steps

🤝 Contributing

This project welcomes contributions and suggestions. Please see CODE_OF_CONDUCT.md for details on our code of conduct.

📄 License

• Code: MIT License
• Documentation: Creative Commons Attribution 4.0 License

🔒 Security

See SECURITY.md for information about reporting security vulnerabilities.

💬 Support

For support and questions, please see SUPPORT.md.

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Microsoft Ignite 2025 | Session PREL15

Building the future of AI-ready applications with Azure