Generative AI with Azure OpenAI - Part 1

Generative AI with Azure OpenAI - Part 1

Develop Generative AI solutions with Azure OpenAI Service


Suppose you want to build a support application that generates content, summarizes text and suggests code. To build this app, you want to utilize the capabilities you see in ChatGPT, a chatbot built by OpenAI that takes in natural language input from a user and returns a machine-created, human-like response.

Generative AI models power ChatGPT's ability to produce new content, such as text, code, and images, based on a natural language prompts. Many generative AI models are a subset of deep learning algorithms. These algorithms support various workloads across vision, speech, language, decision, search, and more.

Azure OpenAI Service brings these generative AI models to the Azure platform, enabling you to develop powerful AI solutions that benefit from the security, scalability, and integration of other services provided by the Azure cloud platform. These models are available for building applications through a REST API, various SDKs, and a Studio interface. This article guides you through the Azure OpenAI Studio experience, giving you a chance ro develop solutions with generative AI.

Azure OpenAI Service

The first step in building a generative AI solution with Azure OpenAI is to provision an Azure OpenAI resource in your Azure subscription. Azure OpenAI Service is currently in limited access. Users need to apply for service access at

Once you have access to Azure OpenAI Service, you can get started by creating a resource in the Azure portal or with the Azure command line interface (CLI).

Create an Azure OpenAI Service resource in the Azure portal

When you create an Azure OpenAI Service resource, you need to provide a subscription name, resource group name, region, unique instance name, and select a pricing tier.

Create an Azure OpenAI Service resource in Azure CLI

To create an Azure OpenAI Service resource from the CLI, refer to this example and replace the following variables with your own:

  • MyOpenAIResource: replace with a unique name for your resource

  • OAIResourceGroup: replace with your resource group name

  • eastus: replace with the region to deploy your resource

  • subscriptionID: replace with your subscription ID

az cognitiveservices account create 
-n MyOpenAIResource 
-g OAIResourceGroup 
-l eastus 
--kind OpenAI 
--sku s0 
--subscription subscriptionID

Azure OpenAI Studio

Azure OpenAI Studio provides access to model management, deployment, experimentation, customization, and learning resources.

You can access the Azure OpenAI Studio through the Azure portal after creating a resource, or at by logging in with your Azure OpenAI resource instance. During the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.

When you first open Azure OpenAI Studio, you'll see a call-to-action button at the top of the screen to deploy your first model. Selecting the option to create a new deployment opens the Deployments page, from where you can deploy a base model and start experimenting with it.

Types of generative AI models

Azure OpenAI includes several types of base models (you could also create customized models)-

GPT-4o & GPT-4 Turbo NEWThe latest most capable Azure OpenAI models with multimodal versions, which can accept both text and images as input.
GPT-4A set of models that improve on GPT-3.5 and can understand and generate natural language and code.
GPT-3.5A set of models that improve on GPT-3 and can understand and generate natural language and code.
EmbeddingsA set of models that can convert text into numerical vector form and are useful in language analytics scenarios such as comparing text sources for similarities.
DALL-EA series of models that can generate original images from natural language.
WhisperA series of models in preview that can transcribe and translate speech to text.
Text to speech (Preview)A series of models in preview that can synthesize text to speech.

Models differ by speed, cost, and how well they complete specific tasks.

In the Azure OpenAI Studio, the Models page lists the available base models (other than DALL-E models) and provides an option to create additional customized models by fine-tuning the base models. The models that have a Succeeded status mean they're successfully trained and can be selected for deployment.

In the next part we will see how to deploy and test models in Azure OpenAI Studio.