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This course explores the Azure Custom Vision service and how you can use it to create and customize vision recognition solutions. You'll get some background info on what the service is before looking at the various steps for creating image classification and object detection models, uploading and tagging images, and then training and deploying your models.

Learning Objectives

  • Use the Custom Vision portal to create new Vision models
  • Create both Classification and Object Detection models
  • Upload and tag images according to your requirements
  • Train and deploy the configured models

Intended Audience

  • Developers or architects who want to learn how to use Azure Custom Vision to tailor a vision recognition solution to their needs


To get the most out of this course, you should have:

  • Basic Azure experience, at least with topics such as subscriptions and resource groups
  • Basic knowledge of Azure Cognitive Services, especially vision-related services
  • Some developer experience, including familiarity with terms such as REST API and SDK

You've made it. That's the end of the course. Congratulations. Let's review a few takeaways from this course. Vision recognition is made possible by the latest developments in a machine learning technique called Deep Learning. There is an infinite set of new technologies using Vision recognition, such as medical imaging, self-driving cars, or even saving animals in danger of extinction. That being said, these AI models are not simple to implement. They require a good amount of data, processing power and data sciences expertise.

Cognitive Services, which include Custom Vision, are Microsoft's response to these complex requirements, by offering models that are easy to use, and cheap to consume. There are two main goals of Vision models: Classification and Object Detection. The fundamental difference between them is that Object Detection also gives a bounding box with the coordinates of each object found in the image. Custom Vision also allows you to customize the classes according to your needs, by interaction with a portal.

The process for new Custom Vision models consists of creating and configure a project, uploading and tagging the images, and then training, evaluating and deploying the model. The deployment can be to a REST endpoint, which can also be accessed by language-specific SDKs, a Container, or to six different edge formats, giving a lot of flexibility to the product.

This concludes our course Implementing Image Classification by Using the Azure Custom Vision Service. I am truly honored that you have gotten this far in it, and I recommend that you continue your journey to learn more about this exciting technology. Thanks for watching!

About the Author

Emilio Melo has been involved in IT projects in over 15 countries, with roles ranging across support, consultancy, teaching, project and department management, and sales—mostly focused on Microsoft software. After 15 years of on-premises experience in infrastructure, data, and collaboration, he became fascinated by Cloud technologies and the incredible transformation potential it brings. His passion outside work is to travel and discover the wonderful things this world has to offer.