Creating Azure AI Services Resource

This guide offers a step-by-step process to create Azure AI Services resources, making it an essential resource for users looking to leverage Azure for artificial intelligence applications.

Hafeez Baig
24 steps
2 minutes

Azure

Type **Azure AI Foundry** in the search bar and click on the **Azure AI Foundry**

**What is Azure AI Foundry?** Azure AI Foundry is a Microsoft platform that helps organizations build, customize, and deploy generative AI applications using prebuilt models and enterprise-grade tools. It supports full-stack AI development with integration into Azure OpenAI, Responsible AI practices, and orchestration frameworks like LangChain or Semantic Kernel. It streamlines AI project lifecycle from experimentation to production at scale.

**Azure AI Foundry** page will open, scroll the page and click on the **Create a resource** button

Create an Azure AI Foundry resource page will open, scroll to the **Resource group** dropdown and select the **azure-ai-rg**

**What is Resource group in Azure?** A Resource Group in Azure is a logical container that holds related resources for an Azure solution, such as virtual machines, databases, and networking components. It helps organize and manage resources collectively based on lifecycle, permissions, and billing. You can deploy, monitor, and delete all resources in a group together, simplifying operations and access control.

You can also create a new resource group, click on the **Create new** link

Type the name for the resource group and click on OK to create a new resource group.

Scroll the page to the Name section, type "**azure-ai-services-in28minutes-azure-01**" in the field **Note:** The Name should be different and unique.

Scroll the page and click on the **Next** button

**Network** tab will open, Choose the **All network, including the internet, can access this resource** radio button.

**What is Inbound Access in Azure AI Service?** Inbound Access in Azure AI Service controls how clients connect to the service over a network. It can be configured to allow public internet access, restrict access using IP rules, or enable secure connections via private endpoints. This ensures secure and controlled access to AI resources.

Scroll the page and click on the **Next** button

**Identity** tab will open, Choose the **System assigned** radio button.

**What is Identity type (System assigned and User assigned)?** In Azure, **Identity Type** refers to how a resource authenticates to other Azure services using **Managed Identities**. - **System-assigned identity** is tied to a single resource and is deleted when the resource is deleted. - **User-assigned identity** is created independently and can be shared across multiple resources. Both simplify secure, password-less authentication within Azure.

Scroll the page and click on the **Next** button

**Data encryption** tab will open, Here you can check box to encrypt data using customer managed key.

**What is Data encryption using customer managed key?** Data encryption using Customer Managed Key (CMK) in Azure allows you to encrypt your data with encryption keys that you control through Azure Key Vault. Unlike Microsoft-managed keys, CMK provides greater control over key lifecycle, rotation, and access policies. This enhances data security and helps meet compliance requirements.

Scroll the page and click on the **Next** button

**Tags** tab will open, Here you can create and add tags.

**What are Tags in Azure?** Tags in Azure are key-value pairs that help you organize and manage Azure resources logically. They enable better cost tracking, automation, and governance by categorizing resources based on attributes like environment, department, or project. Tags are especially useful for reporting and applying policies at scale.

Scroll the page and click on the **Next** button

After the **Validation** is succeed, scroll the page and click on the **Create** button

**Note:** If the deployment fails, try selecting a different region. Azure sometimes restricts free tier deployments or instances in specific regions due to capacity limitations.

After your deployment is complete, click on the **Go to resource** button

Congratulations on successfully creating Azure AI Service!

**Azure AI Service** page will open, here you can view all the details and its resource group.

Click on the **Endpoints** link on the right side to view Endpoints and key details.

**What are Endpoints in Azure AI Foundry?** Endpoints in Azure AI Foundry are secure URLs through which deployed AI models or services can be accessed. They allow applications to interact with foundation models for inference tasks like text generation or classification. Each endpoint includes authentication, configuration, and traffic management settings for reliable and secure access.

**Keys and Endpoints** page will open, here you can view all the key details

Scroll the page to the AI Foundry tab, Here you can find the **AI Foundry API Endpoint.**

Click on the **OpenAI** tab, here you can find the **OpenAI API Endpoints**

Click on the **AI Services** tab, here you can find the **AI Services API Endpoints**

Well done on completing the lab and exploring the API endpoints!

Refer to this lab to continue learning - <https://scribehow.com/shared/Exploring_Azure_AI_Foundry__Creating_Hub_and_Project__VIf2tVybTYCeZ6AOuinV4g>

Creating Azure AI Services Resource

This guide offers a step-by-step process to create Azure AI Services resources, making it an essential resource for users looking to leverage Azure for artificial intelligence applications.

Hafeez Baig
24 steps
2 minutes