Version: 1.1.0 | Last Updated: 2025-06-12

AI Hub Challenge Overview

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Welcome to the IFS AI Hub Challenge. This challenge guides you through designing a secure, scalable, and governed Azure-based AI Hub to centrally manage and control shared AI services for IFS, building on lessons learned from the RAG chatbot implementation.

Table of Contents

Note: Generated using Jekyll’s automatic table of contents feature

Challenge Objectives

  • Analyze IFS’s business context and drivers for a centralized AI Hub
  • Identify and prioritize business, technical, and compliance requirements
  • Design a robust Azure AI Hub architecture aligned with best practices
  • Present and justify your solution to peers

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Challenge Structure

This challenge is divided into four steps:

  1. Step 1: Scenario – Analyze the business scenario and user needs
  2. Step 2: Requirements – Identify and document requirements
  3. Step 3: Design – Design the AI Hub architecture
  4. Step 4: Present – Present and justify your solution

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Challenge Workflow (Visual) 

flowchart LR
    %% Define the flow of the AI Hub Challenge
    Start([Start]) --> Step1
    Step1[Step 1:<br>Scenario] --> Step2[Step 2:<br>Requirements]
    Step2 --> Step3[Step 3:<br>Design]
    Step3 --> Step4[Step 4:<br>Present & Justify]
    Step4 --> End([End])
    
    %% Add descriptions
    classDef step fill:#0072C6,stroke:#025,color:white,stroke-width:2px
    classDef endpoint fill:#5CB85C,stroke:#4CAE4C,color:white,stroke-width:2px
    
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    class Step1,Step2,Step3,Step4 step
    class Start,End endpoint
    
    %% Add annotations
    Step1 -.-> Ann1[Business scenario<br>and use case<br>analysis]
    Step2 -.-> Ann2[Business, technical<br>and compliance<br>requirements]
    Step3 -.-> Ann3[Centralized AI Hub<br>architecture design]
    Step4 -.-> Ann4[Solution presentation<br>and justification]

How to Use This Challenge

  • Work as a team to complete each step in order
  • Use the provided activities, guidance, and success criteria to structure your work
  • Reference the Azure Best Practices for proven recommendations
  • Document your findings and designs for each step