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Building a Health & Fitness AI Advisor with Azure AI Foundry 🏃‍♂️

Welcome to this hands-on, 2-hour workshop where you'll build a practical health and fitness AI advisor using Azure AI Foundry! You'll learn how to deploy an AI model, create an intelligent agent, and evaluate its performance - all through an engaging health-focused use case. 💪

[!NOTE] This documentation is a work in progress. Some sections may be incomplete or subject to change as we continue to improve and expand the workshop content.

flowchart TB
    %% Top: Environment Setup
    ES[Environment Setup:\n• Clone Repo & Set up Python Env\n• Deploy Models & Configure Connections @ ai.azure.com]

    %% Next: Introduction
    I[Introduction:\n1. Authentication\n2. Environment Setup\n3. Quick Start]

    %% Row with Chat Completion & Agent Service side by side
    subgraph WorkshopRow[ ]
      direction LR
      CR[Chat Completion & RAG:\n1. Basic Chat Completion\n2. Embeddings\n3. Basic RAG\n4. PHI-4\n5. DeepSeek R1]
      AS[Agent Service:\n1. Agent Basics\n2. Code Interpreter\n3. File Search\n4. Bing Grounding\n5. Agents + Azure Search\n6. Agents + Azure Functions]
    end

    %% Below: Quality Attributes
    QA[Quality Attributes:\n1. Observability\n2. Evaluation\n3. End-to-End GenAI Ops]

    %% Next: Frameworks
    FW[Frameworks:\n1. RAG + SK + Agents + AI Search]

    %% Finally: E2E Sample
    E2E[E2E AI Native Sample]

    %% Connections
    ES --> I
    I --> CR
    I --> AS
    CR --> QA
    AS --> QA
    QA --> FW
    FW --> E2E

The Use Case: Smart Health Advisory

You'll build an AI agent that can: - Provide personalized fitness guidance - Handle nutrition and exercise inquiries - Access health and wellness resources - Learn from user interactions - Provide safe, accurate health advice with disclaimers

Workshop Timeline (2 hours)

  1. Setup and Model Deployment (30 min)
  2. Quick platform overview
  3. Deploy Azure OpenAI model

  4. Basic configuration and testing

  5. Agent Development (45 min)

  6. Create health advisor agent
  7. Implement health guidance system

  8. Add health knowledge base

  9. Evaluation and Monitoring (45 min)

  10. Set up key metrics
  11. Monitor performance
  12. Analyze and improve responses

Prerequisites

  • Azure subscription with AI services access
  • Python 3.8 or later
  • Basic Python knowledge
  • Text editor or IDE

What You'll Learn

Through this practical example, you'll understand: - How to use the AI Foundry SDK - Model deployment and configuration - Agent creation and management - Performance evaluation and monitoring - Best practices for AI applications

Let's start by setting up your environment!