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.
A practical, stepwise protocol to verify model code and runtime, validate model behavior against benchmarks and external data, quantify and communicate uncertainty, and produce the artifacts needed to judge fitness-for-purpose before deployment.
A practical, step-by-step protocol to validate predictive models, choose baselines, check robustness and generalizability, ensure reproducibility, and produce a clear report with deployment guardrails.
A practical, step-by-step checklist to validate model performance, robustness, generalizability, calibration, fairness, and deployment readiness — plus recommended benchmarks, documentation requirements, and monitoring preparations.