Question

A retail chain wants to classify products on store shelves using cameras that operate in stores with unreliable internet connectivity. The data science team has limited ML coding experience but has thousands of labeled product images. They need a solution that trains a custom image classifier and can run inference directly on in-store edge devices without a constant network connection. Which approach best meets these requirements?

Accepted Answer

Train a custom model with Vertex AI AutoML Image Classification using the 'Edge' model type, then export the model to run on the in-store devices.

Answer

Use the Cloud Vision API label detection endpoint from each in-store device to classify products in real time.

Answer

Train a Vertex AI AutoML Image Classification model with the 'Cloud' hosted model type and call its online prediction endpoint from the devices.

Answer

Build a custom TensorFlow CNN in Vertex AI Workbench notebooks and containerize it for edge deployment.