You are an AI architect at a widely used photo-sharing social media platform. Your organization’s content moderation team currently reviews user-uploaded images and manually removes explicit content. You want to implement an AI service that automatically stops users from uploading explicit images. What should you do?
A Train an image clustering model by using TensorFlow in a Vertex AI Workbench instance. Deploy this model to a Vertex AI endpoint and configure it for online inference. Run this model each time a new image is uploaded to identify and block inappropriate uploads. B Develop a custom TensorFlow model in a Vertex AI Workbench instance. Train the model on a dataset of manually labeled images. Deploy the model to a Vertex AI endpoint. Run periodic batch inference to identify inappropriate uploads and report them to the content moderation team. C Create a dataset using manually labeled images. Ingest this dataset into AutoML. Train an image classification model and deploy into a Vertex AI endpoint. Integrate this endpoint with the image upload process to identify and block inappropriate uploads. Monitor predictions and periodically retrain the model. D Send a copy of every user-uploaded image to a Cloud Storage bucket. Configure a Cloud Run function that triggers the Cloud Vision API to detect explicit content each time a new image is uploaded. Report the classifications to the content moderation team for review. Show Answer Answer Explanation A supervised image-classification model trained on manually labeled images can classify explicit content. Deploying the AutoML model to a Vertex AI endpoint enables online predictions during the upload workflow, so the application can reject inappropriate uploads immediately. Monitoring predictions and periodically retraining helps preserve classification quality as the incoming content distribution changes.
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