Practical ML workflows for discovery: feature engineering, experiment tracking, validation, benchmarking, uncertainty, and interpretability for reproducible research.
A practical, copy-ready notebook template with structured experiment metadata, dataset/version provenance, preprocessing and feature records, training and hyperparameter sections, evaluation reporting, model artifact provenance, and a reproducibility checklist — plus guidance for lightweight tracking integrations.
An interactive, research-focused model card template to capture purpose, data provenance, evaluation results, limitations, interpretability methods, reproducibility steps, licensing, and recommended use cases — designed to make ML models more transparent, reproducible, and easier to evaluate and reuse across teams.
An expanded, practical end-to-end ML workflow for research teams: data contracts and versioning, feature engineering and feature-store practices, training & validation with baselines, interpretability and subgroup checks, model registry, deployment constraints, monitoring, and governance artifacts for reproducibility.
A practical, fillable model card template with a detailed validation checklist covering dataset provenance, training and evaluation procedures, subgroup performance, robustness checks, intended use, limitations, reproducibility requirements, versioning, and monitoring.