Data science reproducibility toolbox

Machine learning for research — workflow template

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.