Discover the essential step to make your Azure Custom Vision model ready for deployment. Learn how publishing to an endpoint enables seamless integration into your applications.
Table of Contents
Question
Your organization, Verigon Corporation, has developed a custom object detection model using Azure’s Custom Vision service. The model has been trained, and you are satisfied with its performance based on the evaluation metrics.
What would be the next step you should take to make the trained Custom Vision model available for use in your application?
A. Retrain the model with a new dataset.
B. Label the images in the model.
C. Ask end users to train the model.
D. Publish the model to an endpoint.
Answer
D. Publish the model to an endpoint.
Explanation
In the given scenario, publishing the model to an endpoint is the next step to make your trained Custom Vision model available in your application. When you publish the model, Azure Custom Vision generates a REST API endpoint that can be used to send images to the model and receive predictions in real time. This step is essential for deploying the model in production and integrating it into your application, allowing it to be accessed and used by your application’s end users. Publishing the model to an endpoint is the final step to make it available for real-time use in your application.
Asking end users to train the model is not the next step. You would not ask end users to train the model, as this is a task best handled by technical experts before the model is deployed. The responsibility of training and refining a machine learning model falls on data scientists or engineers, not end users.
Retraining the model with a new dataset is not the next step. You have already trained the model in this scenario and there is no requirement for retraining the model with a new dataset. Retraining would only be considered if the current model does not meet the desired performance metrics or if new data becomes available that can further improve the model.
Labeling the images in the model is not the next step. Labeling or tagging images is an important step in preparing your dataset, as it involves assigning categories or labels to the images. This step is crucial for training the model but it must be done before uploading the images using the Training API. In this scenario, the model has already been trained.
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