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Answer: Use the model's REST API to send image data and receive predictions.
The most appropriate way to integrate a custom vision model into a web application is by using the model's REST API. This allows the web application to send image data to the model and receive predictions in response. The REST API provides a standardized and secure way to interact with the model, without the need to expose the model's code or trained weights. Embedding the model's code directly into the frontend, running inference locally on the client's device, or re-training the model using the web application's data may not be feasible or efficient in this scenario.
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Your team has developed a custom object detection model using Azure AI Vision to identify and locate objects within images. You need to consume this model in a web application. Which of the following is the most appropriate way to integrate the custom vision model into the web application?
A
Embed the model's code directly into the web application's frontend.
B
Use the model's REST API to send image data and receive predictions.
C
Download the model's trained weights and run inference locally on the client's device.
D
Re-train the model using the web application's data to improve its performance.
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