Azure-AI_Foundry

Module 12: Advanced Scenarios

Overview

This module covers advanced enterprise AI patterns that go beyond basic chat completions — multi-modal AI, batch processing, real-time streaming, and production-grade enterprise architectures for scale, resilience, and cost management.

Advanced Capability Landscape

┌──────────────────────────────────────────────────────┐
│                 Advanced Scenarios                    │
├──────────────┬──────────────┬────────────────────────┤
│  Multi-Modal │  Processing  │  Enterprise Patterns   │
├──────────────┼──────────────┼────────────────────────┤
│ GPT-4o Vision│ Batch API    │ Multi-region HA/DR     │
│ Whisper Audio│ Streaming    │ Retry & rate limiting  │
│ TTS          │ SSE          │ Token management       │
│ Video frames │ Async jobs   │ Cost allocation        │
│ Combined I/O │ Webhooks     │ Reference architecture │
└──────────────┴──────────────┴────────────────────────┘

When to Use Advanced Patterns

Scenario	Pattern	Key Benefit
Analyze images + text together	Multi-modal (GPT-4o Vision)	Unified understanding
Process 100K documents overnight	Global Batch API	50% cost savings
Real-time chat with progressive display	Streaming (SSE)	Better UX, lower perceived latency
Enterprise-grade production system	Multi-region + APIM	99.99% availability
Multi-department AI usage tracking	Cost allocation	Chargeback and governance
Audio transcription at scale	Whisper + Batch	Automated media processing

Module Lessons

Lesson	Topic	Focus
1	Multi-Modal AI	Vision, audio, TTS, combined modalities
2	Batch & Streaming	Global Batch API, SSE, async patterns
3	Enterprise Patterns	HA/DR, retry, token mgmt, cost control

Learning Objectives

After completing this module, you will be able to:

Build multi-modal applications combining vision, audio, and text
Use the Global Batch API for cost-effective large-scale processing
Implement real-time streaming for responsive user experiences
Design multi-region architectures with high availability
Apply retry patterns, rate limit handling, and cost allocation strategies
Plan disaster recovery for production AI systems

Prerequisites

Completion of Modules 1–11 or equivalent experience
Understanding of Azure networking (for multi-region patterns)
Familiarity with async programming concepts

Key Insight

Production AI is an engineering discipline. The difference between a demo and a production system is resilience, observability, cost control, and graceful degradation. This module bridges that gap.