Experiment tracking is the habit of recording every training run as a durable row, not a terminal log. Each run gets an id, and everything needed to explain its result hangs off that id.
Record at minimum:
the git commit of the training code, including preprocessing
the dataset version, or a snapshot of the query that built it
every hyperparameter and the random seeds
the environment: library versions and hardware
metrics on a fixed evaluation set, plus the artifact and its hash
The payoff shows up weeks later. Someone asks why last month's model beat this week's, and the honest answer without tracking is a shrug. With it you diff two rows and find the changed learning rate or the extra week of data. It also ends the classic waste where a strong result cannot be reproduced because nobody recorded the branch.
Rewriting in plainer words…
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