This was basically seven questions stitched into one.
Start by clarifying requirements and scale, then propose a layered architecture with a fast path for real-time scoring and a slow path for model updates. Emphasize trade-offs between latency, precision, and adaptability, and discuss how to measure success with business metrics.
Pro tip: Highlight the importance of a feedback loop and adversarial retraining; mention that at Amazon, precision on hard blocks is critical to avoid false positives that could block legitimate customer emails, so you'd set a high threshold and use human review for borderline cases.
Ask about scale (emails per second), latency distribution, precision/recall targets, and what 'hard blocks' mean. Confirm that p99 latency is end-to-end and understand the cost of false positives vs. false negatives.
Propose a two-tier system: a real-time scoring service using lightweight models (e.g., logistic regression, gradient boosted trees) and a batch pipeline for training and updating models. Include feature extraction, model serving, and decision logic.
Explain how to achieve <50ms p99: use in-memory feature stores, precomputed features, model quantization, and caching. For high precision on hard blocks, use a high threshold and ensemble methods, and route uncertain cases to a secondary review or slower model.
Describe a continuous learning loop: collect user feedback (e.g., mark as spam/not spam), monitor drift, and retrain models frequently. Use adversarial examples and active learning to adapt to new tactics.
List key metrics: precision, recall, F1, false positive rate, latency percentiles, and business metrics like spam catch rate and user complaints. Set up monitoring and alerting for model performance and latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.