Taming the ML Firehose: Scaling Feature Consistency

Modern ML systems, especially large ranking and search models,depend on a steady stream of high-quality, consistent, and fresh features. But in practice the values a model sees in production can differ from what the model was trained on: different sources, slightly different computation logic, or stale ETL jobs cause mismatches. Those mismatches reduce model effectiveness, increase debugging time, and can even cause outages.