Experiment tracking
Experiment tracking covers tools that record training runs, their results and their output files.
In brief
Experiment tracking, in brief.
What it isExperiment tracking covers tools that record training runs, their results and their output files.
Where it sitsOne of 12 categories in the Training and fine-tuning layer. Training and fine-tuning covers the frameworks, libraries and platforms used to train a model from scratch or adapt an existing one to a task, and the tools that track those experiments.
What we track18 products, including Aim, Amazon SageMaker AI managed MLflow, Azure Machine Learning experiment tracking (MLflow), ClearML and Comet (Experiment Management).
The products
18 products in Experiment tracking.
Each name opens a search for that product in our index.
ProductMakerIts own site
Neptune.ai (DEFUNCT: hosted service shut down Mar 2026)Neptune Labs (acquired by OpenAI)docs.neptune.ai
From our census of 2026-11-08. A product listed here is one that belongs in this category; the search answer says whether we have checked it.
Questions
Experiment tracking, answered.
What is Experiment tracking in AI infrastructure?Experiment tracking covers tools that record training runs, their results and their output files.
Which layer of AI infrastructure is Experiment tracking in?Experiment tracking is in the Training and fine-tuning layer. Training and fine-tuning covers the frameworks, libraries and platforms used to train a model from scratch or adapt an existing one to a task, and the tools that track those experiments.
Which products are in Experiment tracking?We track 18 products in Experiment tracking, including Aim, Amazon SageMaker AI managed MLflow, Azure Machine Learning experiment tracking (MLflow), ClearML, Comet (Experiment Management), DagsHub, Databricks Managed MLflow and DVC / DVCLive. The full list is on this page.
Training and fine-tuning
The other categories in Training and fine-tuning.
AutoML & hyperparameter optimizationComputer vision toolkits & platformsDeep learning & ML frameworksDistributed training frameworksFeature storesFederated & privacy-preserving learningFine-tuning platformsML workflow / MLOps platformsOpen-source fine-tuning librariesRL / post-training librariesRL environments & gyms