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1
- Introduction
2
- Generative API is stateless
3
- Regional Azure OpenAI resource
4
- Capacity pools
5
- Responsible AI
6
- Model deployment types
7
- Standard
8
- Global
9
- Network vs inference latency
10
- Intelligent routing
11
- Quota vs available capacity
12
- Data zone and data residency
13
- Availability benefits?
14
- Resource is regional
15
- Multiple regional resources
16
- Enabling in the application
17
- API Management
18
- Prompt caching impact
19
- Provisioned service
20
- PayGo features
21
- PTU features
22
- Azure reservations
23
- Batch service
24
- Summary
25
- Close
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn about Azure OpenAI deployment architectures and resilience strategies in this comprehensive technical video. Explore the stateless nature of generative APIs, regional resource considerations, and different deployment types including standard and global options. Master capacity management through pools, quotas, and intelligent routing while understanding network versus inference latency impacts. Discover data residency requirements, availability configurations, and application integration approaches including API Management. Examine pricing models covering pay-as-you-go features, Provisioned Throughput Units (PTU), and Azure reservations. Gain practical knowledge about prompt caching impacts and batch service capabilities to build robust and scalable Azure OpenAI solutions.

Azure OpenAI Deployment Types and Resiliency - Understanding Models, Capacity, and High Availability

John Savill's Technical Training
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