ModelRefs / Experiment Tracking — AI Glossary
Experiment Tracking — AI Glossary
Logging ML training runs with hyperparameters, metrics, artifacts, and environment information for reproducibility and comparison.
Overview
Experiment tracking tools (MLflow, W&B, Neptune, ClearML, Comet) record every training run: hyperparameters, loss curves, evaluation metrics, model checkpoints, code version (git commit), environment (package versions). Enables systematic comparison of training configurations and reproduction of past results.
Reference details
| Topic | infrastructure |
|---|---|
| Last reviewed | 2026-06-24 |
Related terms
Example: The run you cannot reproduce
A checkpoint from three weeks ago outperforms everything since and nobody can rebuild it: the learning rate is not recorded, the data snapshot moved, the commit is unknown. Tracking exists so that sentence is never true — hyperparameters, metrics, data version and commit captured at run time, because none of it can be reconstructed afterwards.
Commonly confused with
Experiment tracking is not model monitoring. Tracking records training runs before deployment; monitoring watches behaviour after it. Both log, and the vocabulary matters when scoping which problem a tool is meant to solve.
When to use it
Reach for it when:
- More than a handful of training runs, or more than one person doing them
- You need to reproduce or defend a result later
- Comparing configurations systematically rather than by memory
Reach for something else when:
- A single throwaway run
- Logging metrics while leaving data version and commit unrecorded — that is the half that makes it reproducible
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Experiment Tracking — AI Glossary.
Frequently asked questions
What is Experiment Tracking?
Logging ML training runs with hyperparameters, metrics, artifacts, and environment information for reproducibility and comparison.
What concepts are related to Experiment Tracking?
Closely related concepts include mlflow, weights and biases, model registry.