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Workshop: Multimodel Deployment and evaluation with Azure AI Foundry

Information

  • Time: 13:00 - 14:00
  • Level: Intermediate
  • Target Audience: AI Software Developers, Cloud Architects and Technical Decision-Makers, Data Scientists and AI Engineers, Developers looking to build scalable and secure generative AI applications, Professionals interested in comparing LLM performance and optimizing deployment costs

Speakers

  • The proposed workshop is a practical, hands-on demonstration focused on deploying a secure, scalable, and cost-effective multi-model generative AI solution using Azure AI Foundry Serverless Models. The core of the solution is a multi-model web application—the Azure AI Foundry Multi-Model Tool—that allows users to interact with multiple models like GPT-4, DeepSeek, and Phi-3 via serverless API deployments. Participants will gain practical insights into modern serverless AI architecture, including how the frontend (Vite + React) and backend (Python + FastAPI) are securely integrated using Azure Container Apps, Dapr for service-to-service communication, and Azure Key Vault for secret management. The content and exercises will emphasize comparing models by performance and efficiency (e.g., tokens/sec, total time) and ensuring a highly available, secure, and cost-effective deployment.

What will you learn

Understand the architecture and benefits of serverless AI deployment on Azure AI Foundry.
Deploy a multi-model solution using Azure Container Apps, Dapr, and Azure Key Vault.
Build the backend with Python + FastAPI to manage model selection and API integration with Azure AI Foundry.
Evaluate and compare the performance and efficiency of different large language models (e.g., GPT-4, DeepSeek, Phi-3) in a real-time web application.
Implement a secure secret management strategy using Azure Key Vault and Managed Identity

Prerequisites

Own laptop. An active Azure subscription. Azure AI Foundry Hub with an active project (e.g., in East US region). Basic knowledge of Python and web development concepts (e.g., REST APIs, frontend/backend basics). VSCode with the Azure Resources extension installed. Familiarity with foundational AI concepts and Azure services is recommended.

Deliverables Schedule

The complete multi-model web application code (Frontend: Vite + React; Backend: Python + FastAPI). Instructions and files (e.g., Dockerfile, requirements.txt) to deploy the solution on Azure Container Apps. The presentation/blog post presented during the workshop.