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Question 107/112
You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?
Correct Answer: B
* AutoML Edge is a service that allows you to train and deploy custom image classification models for mobile devices12. It supports exporting models as Core ML files, which are compatible with iOS applications3.
* Using a Core ML model directly on the device eliminates the need for network requests and reduces prediction latency. It also minimizes the cost of serving predictions, as there is no need to pay for cloud resources or network bandwidth.
* Option A is incorrect because sending batch requests during prediction does not reduce latency, as the requests still need to be processed by the cloud service. It also incurs more cost than using a local model on the device.
* Option C is incorrect because TFLite models are not compatible with iOS applications. TFLite models are designed for Android and other platforms that support TensorFlow Lite4.
* Option D is incorrect because exposing the model as a Vertex AI endpoint requires network requests
* and cloud resources, which increase latency and cost. It also does not leverage the benefits of AutoML Edge, which is optimized for mobile devices.
* Using a Core ML model directly on the device eliminates the need for network requests and reduces prediction latency. It also minimizes the cost of serving predictions, as there is no need to pay for cloud resources or network bandwidth.
* Option A is incorrect because sending batch requests during prediction does not reduce latency, as the requests still need to be processed by the cloud service. It also incurs more cost than using a local model on the device.
* Option C is incorrect because TFLite models are not compatible with iOS applications. TFLite models are designed for Android and other platforms that support TensorFlow Lite4.
* Option D is incorrect because exposing the model as a Vertex AI endpoint requires network requests
* and cloud resources, which increase latency and cost. It also does not leverage the benefits of AutoML Edge, which is optimized for mobile devices.
- Question List (112q)
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