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Workshop: Designing an End-to-End Azure AI Solution

Version: 1.0
Date: 2025-04-10

Overview

This workshop guides participants through the end-to-end process of designing a secure, scalable, and governed AI solution on Microsoft Azure, aligning with the Microsoft Cloud Adoption Framework (CAF) and Azure Well-Architected Framework (WAF) principles.

The workshop begins with a realistic customer scenario (Innovate Financial Services - IFS). Participants will first design the foundational Azure platform using Azure Landing Zones concepts, including a specialized AI Hub for governed access to AI services. Subsequently, they will design a specific AI workload – an internal RAG (Retrieval-Augmented Generation) chatbot – that leverages this platform foundation.

Goal: To equip participants with the knowledge and design patterns necessary to architect robust enterprise AI solutions on Azure, from the underlying platform infrastructure to the specific AI application components.

Workshop Flow

%% AI Adoption Journey - The "Like a Boss" version
graph LR
    %% Phase 01: Define AI Strategy with starting glyph
    P1[🚀 Phase 01: Define AI Strategy]
    P1a[Motivations]
    P1b[Mission]
    P1c[Objectives]
    
    %% Phase 02: Design AI platform
    P2[Phase 02: Design AI platform]
    P2a[Identity & Access Mgmt]
    P2b[Resource Organization]
    P2c[Networking]
    P2d[Management & Monitoring]
    P2e[Security & Compliance]
    
    %% Phase 03: Design AI workload
    P3w[Phase 03: Design AI workload]
    P3w1[Use Cases]
    P3w2[Prompt Flow]
    P3w3[Components]
    P3w4[Well-Architected Considerations]
    
    %% Phase 03: Design AI environment
    P3e[Phase 03: Design AI environment]
    P3e1[Subscription Setup]
    P3e2[Networking]
    P3e3[AI Model Governance]
    P3e4[Data Governance]
    P3e5[Management & Monitoring]
    P3e6[Security & Compliance]
    
    %% Phase 03: AI Workload Landing Zone Integration Phase
    P3l[Phase 03: AI Workload Landing Zone Integration Phase]
    
    %% Phase 04: End-to-End Review & Justification
    P4[Phase 04: End-to-End Review & Justification]
    
    %% Phase 05: Beer o'clock with beer glyph
    P5[🍺 Phase 05: Beer o'clock]

    %% Link sections within each phase
    P1 --> P1a
    P1 --> P1b
    P1 --> P1c

    P2 --> P2a
    P2 --> P2b
    P2 --> P2c
    P2 --> P2d
    P2 --> P2e

    P3w --> P3w1
    P3w --> P3w2
    P3w --> P3w3
    P3w --> P3w4

    P3e --> P3e1
    P3e --> P3e2
    P3e --> P3e3
    P3e --> P3e4
    P3e --> P3e5
    P3e --> P3e6

    %% Define overall sequence
    P1 --> P2
    P2 --> P3w
    P3w --> P3e
    P3e --> P3l
    P3l --> P4
    P4 --> P5
Loading

Modules

Module 1: Understanding the Business Need

In scope: CAF AI Strategy - Process to develop an AI strategy | AI Plan - Process to plan for AI adoption

Objective: Analyze the customer's (IFS) business drivers, challenges, goals, and specific AI use cases.

Activities:

  • Review the customer story.
  • Identify key objectives, success metrics, and initial high-level requirements.

Materials:


Module 2: Designing the Azure AI Platform Foundation

In scope: CAF: Ready, Govern, Manage, Secure | WAF: Security, Reliability, Operational Excellence

Objective: Design a secure, scalable, and well-governed Azure foundation using Landing Zone principles to support IFS's current needs and future AI adoption. This includes designing a central, secure "AI Hub" for managing and accessing shared AI services like Azure OpenAI and Azure AI Search.

Activities:

  • Define platform requirements (security, governance, connectivity, AI service management).
  • Design the Landing Zone structure (Platform & Application LZs).
  • Architect the AI Hub with private networking (Private Endpoints, secure gateway).
  • Select core platform services.

Key Concepts:

  • Subscription democratization.
  • Identity management.
  • Network topology (Hub-Spoke).
  • Private networking.
  • Azure Policy.
  • Azure Monitor.
  • Centralized AI service governance.

Materials:


Module 3: Designing the AI Workload - RAG Chatbot

In scope: AI Ready – Process to build AI workloads in Azure | Well Architected Framework

Objective: Design the specific "IFS Knowledge Assistant" RAG chatbot application, ensuring it leverages the platform foundation securely and efficiently.

Activities:

  • Define workload requirements.
  • Design the application architecture (UI, backend/orchestration, data sources).
  • Select appropriate Azure services for hosting components (e.g., App Service, ML Endpoints).
  • Design the RAG pipeline.
  • Implement security controls (Managed Identities, Key Vault).
  • Plan for monitoring.
  • Outline deployment strategies (IaC, CI/CD).

Key Concepts:

  • RAG pattern.
  • Prompt Flow.
  • Azure OpenAI.
  • Azure AI Search.
  • App Service.
  • ML Managed Online Endpoints.
  • Application Gateway.
  • VNet Integration.
  • Private Endpoints.
  • Managed Identities.
  • Application Insights.
  • IaC (Bicep).

Materials:


Module 4: End-to-End Review & Justification

Objective: Consolidate the platform and workload designs into a cohesive end-to-end solution.

Activities:

  • Present the final architecture, justifying design choices based on requirements, CAF principles, and WAF pillars.
  • Discuss potential risks and mitigation strategies.

Key Principles Emphasized

  • Cloud Adoption Framework (CAF): Applying guidance across Strategy, Plan, Ready, Adopt, Govern, Manage, and Secure phases.
  • Well-Architected Framework (WAF): Designing solutions considering the five pillars: Cost Optimization, Operational Excellence, Performance Efficiency, Reliability, and Security.
  • Security: Defense-in-depth, private networking, identity-based access control, secure secrets management.
  • Governance: Centralized policy enforcement, cost management, resource organization.
  • Scalability & Reliability: Designing for growth and resilience.
  • Automation: Utilizing Infrastructure as Code (IaC) and CI/CD practices.

Prerequisites

  • Familiarity with fundamental Azure concepts (Subscriptions, Resource Groups, Networking, PaaS services).
  • Basic understanding of AI/ML concepts (LLMs, RAG is helpful but not essential).
  • Experience with architectural design discussions.

Workshop Materials


References

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This repository empowers partners to confidently assist customers in their AI Adoption journey in order to integrate AI into their operations, ensuring alignment with business goals, responsible AI practices, and continuous monitoring to maintain accuracy and reliability.

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