Experiment Tracking

A strong result from hyperparameter tuning is only useful if you can reproduce it later.

Without proper tracking, key details get lost. Code changes, data updates, and undocumented settings make it impossible to recreate the exact conditions that produced the result. What remains is just a checkpoint file with no context.

This is what experiment tracking solves.

It records everything about each training run, hyperparameters, metrics, code versions, data snapshots, and artifacts. With that, results are reproducible, comparable, and traceable back to their exact setup.