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Coding
ML Experiment Tracker
Try itPlan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment def...
What it does
Generate structured experiment plans that can be logged consistently in experiment tracking systems.
The skill document
ML Experiment Tracker
Overview
Generate structured experiment plans that can be logged consistently in experiment tracking systems.
Workflow
- Define dataset, target task, model family, and parameter search space.
- Define metrics and acceptance thresholds before training.
- Produce run plan with version and artifact expectations.
- Export the run plan for execution in tracking tools.
Use Bundled Resources
- Run
scripts/build_experiment_plan.pyto generate consistent run plans. - Read
references/tracking-guide.mdfor reproducibility checklist.
Guardrails
- Keep inputs explicit and machine-readable.
- Always include metrics and baseline criteria.
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