Lets Sail Together with.…..AI : Azure AI Studio

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5 min readJun 2, 2024

Azure AI Studio brings together capabilities from Azure Machine Learning, Azure OpenAI service, and other Azure AI services to provide a single, centralized workspace within which developers can collaborate with data scientists and others to build AI solutions.

Azure AI Studio is a web portal that brings together multiple Azure AI-related services into a single, unified development environment. Specifically, Azure AI Studio combines:

The model catalog and prompt flow development capabilities of Azure Machine Learning service.
The generative AI model deployment, testing, and custom data integration capabilities of Azure OpenAI service.
Integration with Azure AI Services for speech, vision, language, document intelligence, and content safety.

Azure AI Studio includes support for:

Creating Azure AI hubs that provide a collaborative development workspace for data scientists, developers, and others to build AI solutions.
Creating projects in which assets and code for a specific solution are managed.
Scalable, on-demand compute.
Integration with data sources and other cloud services.
Web-based code development environments.
SDKs and CLI libraries for automation.

Azure AI Studio enables teams to collaborate efficiently and effectively on AI projects, such as developing custom copilot applications that use large language models. Tasks you can accomplish with Azure AI Studio include:

Deploying models from the model catalog to real-time inferencing endpoints for client applications to consume.
Deploying and testing generative AI models in an Azure OpenAI service.
Integrating data from custom data sources to support a retrieval augmented generation (RAG) approach to prompt engineering for generative AI models.
Using prompt flow to define workflows that integrate models, prompts, and custom processing.
Integrating content safety filters into a generative AI solution to mitigate potential harms.
Extending a generative AI solution with multiple AI capabilities using Azure AI services.

An AI hub provides a collaborative workspace for AI solution development and management. You need at least one Azure AI hub to use the solution development features and capabilities of AI Studio.

An Azure AI hub can host one or more projects. Each project encapsulates the tools and assets used to create a specific AI solution. For example, you might create a project to enable data scientists and developers to collaborate on building a custom copilot for a business application or process.

An Azure AI hub is the foundation for AI development projects on Azure, and enables you to define shared assets that can be used across multiple projects. You can use AI Studio to perform the following tasks in an Azure AI hub on the Manage page:

Create members and assign them to specific roles.
Create and manage compute instances on which to run experiments, prompt flows, and custom code.
Create and manage connections to resources, such as data stores, GitHub, Azure AI Search indexes, and others.
Define policies to manage behavior, such as automatic compute shutdown.

All AI development in Azure AI Studio is performed within a project. You can create a new project on the Build page in Azure AI Studio, and then use it to:

Deploy large language models to support a chatbot or copilot.
Test models in the chat playground.
Add your own data to augment prompts.
Use prompt flow to define flows that combine models, prompts, and custom code.
Evaluate model responses to prompts.
Manage indexes and datasets for custom data.
Define content filters to mitigate potentially harmful responses.
Use Visual Studio Code in your browser to create custom code.
Deploy solutions as web apps and containerized services.

In addition to the core AI hub resource, other Azure resources are created to provide supporting services. These include

A Storage account in which the data for your AI projects is stored securely.
A Key vault in which credentials used to access external resources and other sensitive values are secured.
A Container registry to store Docker images used by your AI solutions.
An Application insights resource to record usage and performance metrics.
An Azure OpenAI Service resource that provides generative AI models for your applications.

Lets explore now on Azure Studi

Step 1 - Create Azure AI Hub

After the Azure AI Hub has been created, it should look similar to the following image:

View below the Azure resources that have been created.

On the Connections page, observe that a connection to the Azure OpenAI resource you created with your Azure AI Hub named Azure-AI-challenge-service_aoai has been created.