Ml Experiment Tracking

Overview

Use this skill to make ML experiments comparable, reproducible, and audit-friendly.

Scope Boundaries

• Use this skill when the task matches the trigger condition described in description.
• Do not use this skill when the primary task falls outside this skill’s domain.

Shared References

• Reproducibility metadata rules:
  • references/reproducibility-metadata-rules.md

Templates And Assets

• Tracking schema template:
  • assets/experiment-tracking-schema-template.md

Inputs To Gather

• Required metadata fields (code/data/config/artifacts).
• Tooling constraints for run logging and artifact storage.
• Reproducibility requirements by project risk level.
• Comparison dimensions for model decisions.

Deliverables

• Experiment tracking schema and mandatory fields.
• Run comparison protocol.
• Reproducibility verification checklist.

Workflow

1. Define required metadata with assets/experiment-tracking-schema-template.md.
2. Validate sufficiency using references/reproducibility-metadata-rules.md.
3. Enforce run logging and artifact lineage.
4. Re-run selected experiments from metadata only.
5. Publish reproducibility confidence and gaps.

Quality Standard

• Every decision-grade run is reproducible.
• Artifact lineage is complete and queryable.
• Comparison views are consistent across runs.

Failure Conditions

• Stop when runs cannot be reproduced from recorded metadata.
• Stop when artifact lineage is incomplete.
• Escalate when tracking gaps block release decisions.