Experiment Tracking

Experiment tracking is the discipline of logging every training and evaluation run so results are comparable and reproducible. Each run records its hyperparameters, code commit, dataset version, resulting metrics, and output artifacts. Months of trial and error become a searchable table instead of a folder of screenshots and a fading memory of which settings actually worked. Machine learning is relentlessly iterative — you try dozens of configurations, and the winner is meaningless if you can't recreate it. Tracking answers 'which change caused the score to jump?' and lets a teammate reproduce your best run exactly. Tools include MLflow, Weights & Biases, and Neptune. For teams building on LLMs the same idea applies to prompt and model experiments: log the prompt version, model name, temperature, and eval scores for every attempt. Practical note: adopt tracking on day one, not after your fortieth run — the value is entirely in the history you would otherwise have thrown away.