Aquarium Learning

We help ML teams improve their models by improving their datasets

Y CombinatorSummer 2020AcquiredB2BInfrastructureDeep LearningDeveloper ToolsGenerative AIMachine Learning

What they do

Problem

ML teams struggle to improve model performance because their training datasets contain quality issues, anomalies, and missing diversity.

Solution

A dataset-focused tooling platform that helps teams detect dataset anomalies and failure patterns and fix them by editing or adding the right data for better retraining outcomes.

Who it's for

Machine learning teams at companies building and retraining ML models.

Founders

Peter Gao

Founder

Peter was an early employee (#18) at Cruise, where he built a large part of a self driving car from scratch. Before that, Peter did research on deep learning at UC Berkeley. Before that, he interned at Pinterest and Khan Academy, doing a mix of ML and web work. Now cofounder at Aquarium!

LinkedIn · X

Quinn Johnson

Founder

Quinn is an engineer/manager who picked a *fantastic* time to co-found a company making deep learning pipelines that improve themselves. Before that he was at Ouster (leading data engineering / data viz), Cruise Automation (leading ML data engineering + labeling), and Graphistry (1st engineering hire, so a bit of everything). Working on self-driving cars has given him an irrational hatred for trees and shrubbery.