documenting-experiment-results

Standardize machine learning experiment documentation by enforcing a strict mapping between output artifacts and repository design files. This generates version-stamped JSON logs and updated model cards, ensuring training and evaluation outcomes are traceable and reproducible.

A durable design ledger for ML training runs is maintained by automatically mapping experiment outputs to the required JSON and Markdown documentation structure. This ensures all checkpoints and evaluations are properly recorded, versioned, and linked to project architecture invariants.

Model cards synchronize with promoted checkpoints by ensuring every promotion is accompanied by an updated MODEL_CARD.md and a summary in the repository README. This records the new experiment metrics and recipe for complete evidence-grounded research.

Yes, you can apply this standardization to benchmarking workflows requiring evidence-grounded research. It ensures experiment outcomes are traceable and compliant with project architecture invariants by mapping output artifacts to version-stamped JSON logs.

Experiment tracking fails with fragmented documentation because training runs, evaluations, and checkpoints are not properly recorded or linked to the design ledger. Enforcing a strict mapping between output artifacts and repository files resolves this reproducibility gap.

No specific dependencies are required to version stamp JSON logs and update experiment documentation. The process operates directly on repository design files to enforce traceability and compliance with project architecture invariants.