Automated Data Preprocessing And Normalization

via “data preprocessing and feature engineering within sql”

Postgres with GPUs for ML/AI apps.

Unique: Implements preprocessing as native SQL functions that operate on table columns in-place, with transformation parameters stored in the database for reproducible application during inference. Eliminates data movement and ensures preprocessing consistency between training and serving.

vs others: Simpler than Pandas + scikit-learn pipelines because it's a single SQL call; more reproducible than external preprocessing because parameters are stored in the database; faster than exporting data for preprocessing because it happens in-process.