A strong feature engineering pipeline should maintain clean separation between data ingestion, cleaning, and transformation steps.
Note: This article references the academic demonstration version of the pipeline.
Some implementation details have been simplified or removed for IP protection.
Full implementation available under commercial license.
A strong feature engineering pipeline should maintain clean separation between data ingestion, cleaning, and transformation steps. In this project, the code under dependencies/transformations implements discrete, reusable operations for tasks like aggregating severity data (dependencies/transformations/[medical_transform_removed].py) or dropping rare DRGs (dependencies/transformations/[medical_transform_removed].py). Each script references a corresponding YAML config (for instance, configs/transformations/[medical_transform].yaml), ensuring typed, standardized parameters.
Tracking these transformations through version control in Git and DVC guarantees consistent behavior between training and inference. A single misalignment in feature transformations can degrade model performance in production, so each step is reviewed and validated using typed dataclasses and logs. Metadata capture functions (dependencies/metadata/calculate_metadata.py) add further visibility into each feature’s lineage. When transformations stay modular and thoroughly documented, even complex pipelines remain flexible, reproducible, and easy to extend.