# Ds Experiment Tracking

> Patterns for maintaining reproducibility in machine learning experiments.

- Skill: `jcorpac/ds-experiment-tracking` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jcorpac/ds-experiment-tracking`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jcorpac/ds-experiment-tracking/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: jcorpac (https://skillmd.com/u/jcorpac)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jcorpac/ds-experiment-tracking

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# Experiment Tracking

The goal of experiment tracking is to be able to recreate any model you've built by recording the environment, data, and parameters.

## What to Track
- **Metadata**: Date, Author, Git Hash.
- **Parameters**: Hyperparameters like `learning_rate` or `n_estimators`.
- **Artifacts**: Saved model files (`.pkl`, `.onnx`), feature importance plots.
- **Metrics**: Validation scores from your evaluation runs.

## Tools of the Trade
- **MLflow**: The industry standard for local/remote tracking.
- **Weights & Biases**: Best for deep learning and visualizations.
- **DVC**: For versioning the actual datasets.

## Implementation Pattern
Always use a centralized dictionary or YAML file to store your experiment config before running your training script.