The problem it solves
Training is a search over choices. Without a record you cannot answer which run produced the model you shipped, or why one beat another. Experiment tracking captures the full context of every run.
What gets logged
- Parameters such as learning rate, batch size, and architecture choices.
- Metrics such as loss and accuracy, logged over time.
- Artifacts such as checkpoints, plots, and the final model file.
- Metadata such as the code commit, dataset version, and environment.
Why it matters
A tracker turns scattered runs into a searchable, comparable table. You can sort by validation metric, filter by a parameter, and reproduce the winner exactly because the commit and data version are attached.
Tools like MLflow and Weights and Biases provide this logging plus a dashboard to compare runs side by side.
Key idea
Experiment tracking records the parameters, metrics, artifacts, and metadata of every run so results stay comparable and the shipped model is reproducible.