Experiment Tracker

Overview

Transforms chaotic ML experimentation into organized, reproducible research. Every experiment is logged, versioned, and tied to a SpecWeave increment, ensuring team knowledge is preserved and experiments are reproducible.

Problem This Solves

Without structured tracking:

• ❌ "Which hyperparameters did we use for model v2?"
• ❌ "Why did we choose XGBoost over LightGBM?"
• ❌ "Can't reproduce results from 3 months ago"
• ❌ "Team member left, all knowledge in their notebooks"

With experiment tracking:

• ✅ All experiments logged with params, metrics, artifacts
• ✅ Decisions documented ("XGBoost: 5% better precision, chose it")
• ✅ Reproducible (environment, data version, code hash)
• ✅ Team knowledge in living docs, not individual notebooks