Machine learning & analytics for research

Practical ML workflows for discovery: feature engineering, experiment tracking, validation, benchmarking, uncertainty, and interpretability for reproducible research.

Protocol

Model verification, validation, and uncertainty protocol

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.

Protocol
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Protocol

Model validation & benchmarking protocol

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.

Protocol
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