ML Data Pipeline Overview Use this skill for data ingestion, validation, preprocessing, feature engineering, dataset versioning, feature stores, batch and streaming pipelines, and data-quality monitoring. In ML, data pipeline correctness is often more important than model sophistication. A pipeline must produce leakage-safe training data and consistent serving features. Data Data Pipeline Invariants - Raw data is immutable or snapshot-addressable. - Schemas, statistics, and quality expectations are validated before training and serving. - Transformations are versioned and reproducible. - Spli…