## ML System Design: Ads Ranking (e-commerce) Design an online **ads ranking** (ad “re-ranking”) system for an e-commerce app. The system receives a request when a user opens a page/feed and must select and order a set of candidate ads to show. ### Requirements - **Objective:** maximize long-term business value (e.g., revenue), while maintaining user experience - **Latency:** low-latency online ranking (tens of milliseconds to a few hundred ms, depending on assumptions) - **Scale:** many users/requests, many advertisers/items - **Modeling topics to cover:** - Feature engineering (user, item/ad, context, cross features) - Model architecture choices for ranking - **Calibration** of predicted probabilities (e.g., CTR/CVR) and why it matters - **Evaluation:** offline metrics + online A/B testing and guardrails Explain your end-to-end design: candidate generation, ranking/re-ranking, training pipeline, serving, and monitoring.