Learn how to seamlessly integrate Azure Custom Vision’s trained image classification models into your applications using the REST API. Step-by-step guide for real-time predictions and efficient AI deployment.
Table of Contents
Question
Your organization, Verigon Inc., has developed a custom image classification model using Azure Custom Vision. The model has been trained, evaluated, and published. You want to integrate this model into your application to perform real-time image classification.
What is the next step to consume your Custom Vision model in your application?
A. Use a REST API to integrate the AI model into your application.
B. Submit images to the AI model for receiving and processing predictions.
C. Label the images in the AI model.
D. Use Azure Cognitive Search to integrate the AI model into your application.
Answer
A. Use a REST API to integrate the AI model into your application.
Explanation
Your next step in the given scenario would be to use a REST API to integrate the AI model into your application. After your Custom Vision model has been trained, evaluated, and published, the next step is to integrate it into your application. You can do this by using the REST API provided by Azure Custom Vision. This allows your application to send images to the model’s endpoint and receive real-time predictions, making the model accessible for use in your application.
Your next step in the given scenario would not be to submit images to the AI model for receiving and processing predictions. Submitting images for prediction is part of the process, but this step assumes that the integration of the model into your application has already been done. Before submitting images, though, you need to establish a way for your application to interact with the AI model, which involves setting up the REST API. Therefore, this is not the next step in the process.
Your next step in the given scenario would not be to label the images in the AI model. Labeling images is a critical step during the training phase of your Custom Vision model. This step involves tagging images with the correct labels to teach the model how to classify them. However, since the model has already been trained, evaluated, and published, labeling the images is not the next step at this stage.
Your next step in the given scenario would not be to use Azure Cognitive Search to integrate the AI model into your application. Azure Cognitive Search is designed to create searchable indexes from large datasets, primarily text-based content. It can be used after predictions have been made to create searchable indexes. It is not designed for directly integrating AI models into applications.
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