To my understanding Aquarium is focused on curation of datasets rather than labeling (e.g. finding edge cases in data, etc.) and have built an awesome product around that. Aquarium works well in domains that are further along on the ML adoption curve -- particularly AV -- where finding and solving edge cases in your data is typically the key constraint. Immature domains (e.g. medical, agtech, etc.) is still stuck at the data labeling stage, and due to the expensiveness of the annotators have a hard time scaling.

Our focus is on minimising human involvement in the data annotation process to make it more efficient and facilitate ML development in these more specialised fields.