/track

name: track
description: Track an ML experiment by logging parameters, metrics, and artifacts for comparison.

Track an ML experiment by logging parameters, metrics, and artifacts for comparison.

Steps

1. Define the experiment metadata: 2. Log hyperparameters: 3. Log metrics during and after training: 4. Save artifacts: 5. Record environment details: 6. Tag the experiment with status (running, completed, failed). 7. Store results in a structured format for later comparison.

Format

Experiment: <name>
Date: <timestamp>
Hypothesis: <what is being tested>
Params: { learning_rate: X, batch_size: Y, ... }

Rules

• Always log random seeds for reproducibility.
• Record the exact dataset version used.
• Never overwrite previous experiment results.