Design comment-likelihood prediction platform

## Scenario You’re building an ML platform component that serves a model to predict **the likelihood that a user will comment on a given post**. The interviewer says you can treat the model as a **black box** (you don’t need to pick a specific architecture); focus on **ML infrastructure**: feature pipelines, feature store, training/serving, inference, and production concerns. ## Goals Design an end-to-end system that supports: - Offline training data generation and model training - Online inference (real-time scoring) for product surfaces (e.g., feed ranking, notification candidate scoring) - A feature store strategy (offline + online) - Monitoring, logging, and iteration ## Requirements & constraints (you may make reasonable assumptions) - High QPS online scoring (potentially tens of thousands/sec) - P95 latency budget for scoring: e.g., 50–150 ms end-to-end (state your assumption) - Freshness: some features need near-real-time updates (seconds to minutes) - Avoid training/serving feature skew - Handle cold start (new users/posts) - Support A/B testing and safe rollout ## Deliverables Explain: 1. Data sources and event logging 2. Feature engineering and feature store design 3. Training pipeline and dataset versioning 4. Online inference architecture (batch vs real-time, caching, fallbacks) 5. Monitoring (data + model), retraining triggers, and reliability considerations