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Explore how to create and use a custom vision resource in Azure to train and predict models for identifying individual products, including pricing, resource setup, and uploading and tagging images.
Create and configure a Custom Vision AI project, selecting image classification with multi-label, choosing domain general A1, then upload and label images before training the model.
Learn to retrieve people, objects, smart crops and read using C#, extract bounding boxes and confidence, join coordinates for display, and loop through tags, lines, and words for detailed output.
Pause at a breakpoint, inspect variables with a watch, drill down into results, and step through code using debugging tools for Python and C# to locate errors.
Create a custom vision prediction client in C# with a key and endpoint, then classify or detect images using project ID and iteration name, and output tag name and probability.
Complete the course by analyzing images with a custom image analysis model, generating captions, dense captions, and tags using C# and Python.
This course goes through all of the skills required for the Microsoft Applied Skills: Build an Azure AI Vision solution.
This can also help with Microsoft AI-102 "Designing and Implementing a Microsoft Azure AI Solution" exam, with "Implement computer vision solutions".
Please note: This course is not affiliated with, endorsed by, or sponsored by Microsoft.
Sections 5 and 6 have been re-recorded in line with the new version 1.0.0 of Azure.AI.Vision.ImageAnalysis, and with Visual Studio 2026.
Note: The Microsoft Applied Skill will no longer be offered after 30 January 2026.
In this 2½ hour course we’ll cover the skills that you need for the APL-3004 Microsoft Applied Skills credential for Azure AI Vision.
The tasks that you need to perform to get this skill are:
Create a computer vision resource and analyze images. We’ll create a computer vision resource in a free Azure subscription, and use it to analyze images. We’ll create captions and dense captions, looking at the results in text and in JSON format, before looking at other features.
Create, train, evaluate and consume a custom image analysis model. After that, we’ll create a custom image analysis model. We will train it to recognise three different types of products, and then test it so that it can identify them in new photos.
Programming in C# and Python. Finally, we’ll find out how to analyse images in code. We’ll use Visual Studio and customise code to connect to both the computer vision and the custom image resources, and use them to analyse images and return the analysis.
We’ll go through several practical examples, so you can see how you can analyse your own images.
By the end of the course, you'll be much more confident about using building an Azure AI Vision solution and perhaps even take the official Microsoft assessment. That would look great on your CV or resume.