Aquarium sifts through massive datasets and focuses your attention on the most problematic cases. Sort examples by loss to uncover labeling errors and analyze model embeddings to find patterns of model failures.
Trawl through unlabeled datasets and find rare examples with similarity search, then submit the data to your labeling provider with the click of a button. Get the most model improvement for the lowest labeling cost.
Group problematic datapoints into issues. Share issues with teammates to discuss how to handle them. Take the proper corrective action to resolve these issues. Verify that issues are fixed after your model is retrained. All without leaving Aquarium.
Access datasets from anywhere. Share live links to interesting datapoints. View experiment results from teammates and compare them to your own. Track changes to your datasets and your models over time.