Intermediate · Research

Experiment Tracking

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Systematically recording the inputs, code, parameters, and outputs of every ML experiment.

Technical Definition

Systematically recording the inputs, code, parameters, and outputs of every ML experiment.

How it works

Tools like Weights & Biases, MLflow, and Neptune log hyperparameters, metrics, datasets, model artifacts, and system info. Without tracking, ML teams lose months rediscovering what they already learned. With it, every experiment becomes a reusable building block.

Related Concepts

• Hyperparameter Tuning — The process of finding optimal configuration values that control model training, such as learning rate, batch size, and architecture choices.
• Data Versioning — Tracking changes to datasets the way Git tracks changes to code.
• Reproducibility — The ability for others to re-run an experiment and obtain the same results.