Scalable data classification for security and privacy

Introduced a scalable data classification system using multiple data signals, a scalable system architecture, and machine learning to detect semantic types within Facebook at scale. This system handles both persisted and non-persisted data, employing Bernoulli sampling, feature extraction, and a prediction service for offline data stores. For online data, it provides a real-time API for classification predictions. The system integrates heuristic rules and a modular PyTorch-based ML model for unstructured data, aiming to improve accuracy and enable automated policy enforcement.