Data Science Engineering Suite - Quick Reference

This skill turns raw data and questions into validated, documented models ready for production:

• EDA workflows: Structured exploration with drift detection
• Feature engineering: Reproducible feature pipelines with leakage prevention and train/serve parity
• Model selection: Baselines first; strong tabular defaults; escalate complexity only when justified
• Evaluation & reporting: Slice analysis, uncertainty, model cards, production metrics
• SQL transformation: SQLMesh for staging/intermediate/marts layers
• MLOps: CI/CD, CT (continuous training), CM (continuous monitoring)
• Production patterns: Data contracts, lineage, feedback loops, streaming features

Modern emphasis (2026): Feature stores, automated retraining, drift monitoring (Evidently), train-serve parity, and agentic ML loops (plan -> execute -> evaluate -> improve). Tools: LightGBM, CatBoost, scikit-learn, PyTorch, Polars (lazy eval for larger-than-RAM datasets), lakeFS for data versioning.

Quick Reference