Advent of 2024, Day 4 – Microsoft Azure AI – Deployment in Azure AI Foundry
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In this Microsoft Azure AI series:
- Dec 01: Microsoft Azure AI – What is Foundry?
- Dec 02: Microsoft Azure AI – Working with Azure AI Foundry
- Dec 03: Microsoft Azure AI – Creating project in Azure AI Foundry
When you are in Azure AI Foundry, on the left navigation bar, select “Model Catalog”.
For this demo, I will be selecting multimodal model “gpt-4” that can work with images and text.
Click “> Deploy” and select the deployment type and also customize the deployment details.
Deployment Types are:
- Global standard (Global deployments are available in the same Azure OpenAI resources as non-global deployment types but allow you to leverage Azure’s global infrastructure to dynamically route traffic to the data center with best availability for each request. Global standard provides the highest default quota and eliminates the need to load balance across multiple resources.)
- Standard (Standard deployments provide a pay-per-call billing model on the chosen model. Provides the fastest way to get started as you only pay for what you consume. Models available in each region as well as throughput may be limited. Standard deployments are optimized for low to medium volume workloads with high burstiness. Customers with high consistent volume may experience greater latency variability)
- Global Batch
- Provisioned-managed (Provisioned deployments allow you to specify the amount of throughput you require in a deployment. The service then allocates the necessary model processing capacity and ensures it’s ready for you. Throughput is defined in terms of provisioned throughput units (PTU) which is a normalized way of representing the throughput for your deployment. Each model-version pair requires different amounts of PTU to deploy and provide different amounts of throughput per PTU)
Once deployment is finished, the model is ready to be consumed.
There are many ways to so this. Via Python SDK or via playground. Let’s click “Open in playground” and start using the deployed model.
In this demo, I have used the instructions and context. I love Seinfeld, and I have used this context:
Write in a funny manner and relaxed tone. I love to watch Seinfeld and give answers with subtile note to conversations in Seinfeld. Include also some references to Seinfeld in your answers.
And asked most basic “Seinfeldian” question “Where are we eating tonight”. 
Tomorrow we will start with tweak parameters and will add our own data in playground using Azure AI Foundry.
All of the code samples will be available on my Github.
Thanks for visiting r-craft.org
This article is originally published at https://tomaztsql.wordpress.com
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