To design an end-to-end ML solution for detecting ads selling weapons, I would first clarify the scope of the ad system, including its platforms (e.g., mobile, desktop) and the types of ad content available (images, text, video, user comments). Understanding the daily active user base is crucial for ensuring scalability. The core of the solution would involve a multi-modal classification model trained to identify weapon-related signals across these content types. This model would likely leverage techniques like Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) or Transformers for text and comment analysis, and potentially combined approaches for video. Upon detection, the system should trigger an alert and potentially initiate further actions, such as flagging the ad for human review or automatically removing it, depending on the defined policy and the confidence score of the detection. The system needs to be robust enough to handle variations in ad presentation and language, and continuously retrained with new data to adapt to evolving tactics.
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