Data Science Engineering Suite

Frame the decision and prediction timestamp before choosing models; features must exist at scoring time.

Quick Reference

Need	Default Direction
reproducible Python workflow	`uv` plus scripts or git-friendly notebooks (marimo for reactive/diffable notebooks)
fast local analysis	DuckDB plus Polars; check installed API against Polars docs before adapting older examples
data contracts	Pandera or GX Core at dataset boundaries
tabular baseline	linear or logistic model plus tree-based candidate; add a tabular foundation model only after the licence and size lookup in `references/modelling-patterns.md` §3.1
feature engineering	explicit train-serve-safe transforms
unlabeled text corpus	embed -> UMAP -> HDBSCAN -> c-TF-IDF; use topic-level LLM labels when document-level labels are unnecessary
tuning	Optuna only after the baseline is stable
evaluation	slices, threshold, calibration, uncertainty
handoff	model card, evaluation report, failure modes, monitoring expectations