Learnings include creating Azure Open AI services, deploying GPT models, interacting with GPT in Playground, and optimizing model performance with parameters.
The Microsoft team has recently launched an Open AI challenge, in which a developer can learn how to build Azure AI solutions and apps.
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Experience working with Azure and Azure portals.
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An understanding of generative AI.
Step 1: Navigate to the Azure Portal
Search for Azure Open AI and fill out the following details
For this exercise tag names are not required. But in a production environment, it should be added as it's a best practice.
On the left side open Models > Select “gpt-35-turbo” > Deploy as highlighted below
The below model list will be populated based on the access provided by the Microsoft team.
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GPT-4 models: Latest generation for natural language and code completions from prompts.
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GPT 3.5 models: Generate language and code completions, with turbo versions for chat interactions.
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Embedding models: Convert text to numeric vectors, useful for language analytics.
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DALL-E models (preview): Generate images from natural language prompts, automatically available without explicit deployment in Azure OpenAI Studio.
Input the following prompt in the chat to see if the service is working or not. It may take 5–10 minutes, please be patient.
Please find below the response to the above prompt.
The whole setup can also be done via the CLI using the following command
az cognitiveservices account deployment create \
-g OAIResourceGroup \
-n MyOpenAIResource \
--deployment-name MyModel \
--model-name gpt-35-turbo \
--model-version "0301" \
--model-format OpenAI \
--sku-name "Standard" \
--sku-capacity 1
I haven’t tried the CLI approach but just wanted to mention the other alternative.
Play with playground parameters to change the performance of the model.
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How to create an Azure Open AI Service.
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How to deploy GPT models.
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How to converse with GPT in the Playground.
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Different approaches to deploying an Open AI service on Azure.
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Playground parameters are available to improve the performance of the model.
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