Design a Spam Filter for Email/Messaging

To design a real-time spam filter, I would first clarify that we need sub-100ms latency for millions of daily messages. The architecture starts with an ingestion layer where incoming emails are queued. We then extract features in parallel: structural checks for headers, statistical analysis of sender domains, and semantic vectorization of the email body using transformer models. These features feed into a dual-model system. A lightweight gradient boosting model handles immediate classification for known patterns, while a neural network processes complex, novel content. Crucially, we deploy these models on Kubernetes clusters behind an auto-scaling load balancer to handle traffic spikes. For the feedback loop, when a user marks an email as spam, this signal is streamed to a feature store. We use this data to trigger incremental retraining jobs overnight, ensuring the model adapts to new phishing campaigns. We must also monitor drift; if the false positive rate exceeds 0.1%, the system automatically rolls back to the previous version. This approach balances high throughput with continuous adaptation, mirroring the robust infrastructure Microsoft relies on for services like Outlook.