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Answer: AutoML Vision Edge, Cloud Vision API
AutoML Vision Edge is the correct choice because it allows the model to be deployed on edge devices, such as mobile phones, making it ideal for use at flea markets with unreliable internet connectivity. The Cloud Vision API is also a suitable option as it provides pre-trained models for image analysis that can be accessed via the cloud, offering a backup solution when edge deployment is not feasible. Vision AI and Video AI are not suitable for this scenario as they are designed for pre-trained models and video content analysis, respectively. AutoML Vision, while capable of training custom models, is a cloud solution and does not meet the requirement for edge deployment. For more details, refer to the following resources: [AutoML Vision Edge Quickstart](https://cloud.google.com/vision/automl/docs/edge-quickstart), [Cloud Vision API Documentation](https://cloud.google.com/vision/docs).
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Your company has developed an innovative auction site specializing in furniture from various historical periods. You are tasked with creating a machine learning model that can accurately identify the period, style, and type of furniture from photos. Additionally, the model should assess whether a piece is of significant historical or aesthetic value, warranting a detailed estimate. After successful development, your manager requests that this service be accessible to mobile users at flea markets, where internet connectivity may be unreliable. The solution must be cost-effective, scalable, and capable of running on edge devices. Considering these requirements, which of the following Google Cloud services would be most suitable for deploying this model? (Choose two options if option E is available)
A
Vision AI
B
AutoML Vision Edge
C
Video AI
D
AutoML Vision
E
Cloud Vision API