Individual skill
experiment-tracking-setup
Guide for experiment tracking tool setup (MLflow, Weights & Biases, etc.), reproducibility assurance, model registry, and experiment comparison methodology. Use this skill for ML experiment management involving 'experiment tracking', 'MLflow', 'W&B', 'Weights and Biases', 'reproducibility', 'model registry', 'experiment comparison', 'hyperparameter logging', etc. Enhances the training-manager's experiment management capabilities. Note: model architecture design and feature engineering are outside this skill's scope.
en/31-ml-experiment/.claude/skills/experiment-tracking-setup/skill.md
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# Experiment Tracking Setup — Experiment Tracking and Reproducibility Guide
A practical guide for ML experiment tracking, reproducibility assurance, and model version management.
## MLflow Setup
### Basic Structure
```python
import mlflow
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("order-prediction")
