Description
Azure AI Vision is a cloud-based service that provides advanced computer vision capabilities powered by machine learning and artificial intelligence. It can analyze visual content in images and videos, detect objects, extract printed or handwritten text (OCR), generate image descriptions, and moderate content.
This service unifies the previous Computer Vision and Cognitive Services APIs into a modern experience. With Azure AI Vision, developers can easily build intelligent applications that understand and interpret visual data.
In this guided lab, you’ll learn how to create an Azure AI Vision resource in the Azure portal. You’ll configure essential settings, review deployment progress, and verify that the resource is successfully provisioned for future integration with image and video analysis applications.
Objectives
In this lab, you will:
- Learn how to create an Azure AI Vision resource in the Azure portal.
- Configure basic settings such as resource group, region, and pricing tier.
- Explore networking, identity, and tag configurations.
- Verify the deployment and confirm the resource creation.
Subscribe to access Azure PlayCloud LabsLab Steps
Creating the Azure AI Vision Resource
1. Navigate to Azure AI Vision or Computer Vision by typing it in the Azure portal search bar or selecting it from the homepage.
2. Start creating a new resource by clicking Create → Azure AI Vision at the top menu bar or the Create button at the center of the service page.
Configuring the Resource
- On the Basics tab, select the following configurations:
- Resource group: azure-lab-rg-JBHrLfL6Wo6r
- Region: Central US
- Pricing tier: Standard (S1)
2. Once you’ve completed the required configurations, feel free to explore the other tabs, such as Access configuration, Networking, and Tags. The default settings in these sections are typically sufficient and can be left unchanged.
3. When you’re ready, click the Create button to initiate the Vision deployment.
- If validation fails, check the highlighted fields for missing or incorrect values. Adjust the configuration and re-run validation until it passes.
4. After deployment completes, click Go to resource to open the Azure AI Vision instance.
Deployment Overview
You’ll be redirected to the Deployment Overview blade, where you can monitor real-time progress.
Key Elements to Review:
- Status: Displays the deployment state (e.g., Accepted, In Progress, Succeeded, or Failed).
- Start Time: Indicates when the deployment began.
- Resource Group: Identifies where your resource is deployed.
- Subscription: Specifies which Azure subscription is used for billing and permissions.
- Correlation ID: A unique trace identifier that is helpful for support or diagnostics.
- Resource Details: Lists all deployed components and their operation status.
You can also view updates by clicking the bell icon (Notifications) in the top-right corner of the Azure portal.
That’s it! Using the Azure portal, you’ve successfully created and deployed an Azure AI Vision resource. In this lab, you practiced navigating Azure services, configuring core resource settings, monitoring deployment progress, and validating access credentials. Your Azure AI Vision resource is ready to power intelligent applications that recognize images, analyze visual content, and deliver real-time insights.