Experiment Tracking

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

Without tracking, the details disappear quickly: data snapshot, feature code, tokenizer version, random seed, container image, evaluation split, and even the exact command used to launch training. What remains is a checkpoint file with no lineage.

This is what experiment tracking solves.

Experiment tracking records the inputs and outputs of each training run: hyperparameters, metrics, code versions, data snapshots, artifacts, and environment metadata. With that, results become comparable and traceable instead of anecdotal.