Topic 1 Question 194
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?
Train the model by using AutoML, and register the model in Vertex AI Model Registry. Configure your mobile application to send batch requests during prediction.
Train the model by using AutoML Edge, and export it as a Core ML model. Configure your mobile application to use the .mlmodel file directly.
Train the model by using AutoML Edge, and export the model as a TFLite model. Configure your mobile application to use the .tflite file directly.
Train the model by using AutoML, and expose the model as a Vertex AI endpoint. Configure your mobile application to invoke the endpoint during prediction.
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- 正解だと思う選択肢: B
No-code model development: AutoML Edge provides a no-code interface for model training, aligning with the requirement. Optimized for mobile devices: Core ML is specifically designed for iOS devices, ensuring efficient inference and low latency. Offline capability: The app can run predictions locally without requiring network calls, reducing costs and ensuring availability even without internet connectivity. No ongoing endpoint costs: Unlike using a Vertex AI endpoint, there are no extra costs associated with hosting and serving the model.
👍 1pikachu0072024/01/12
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