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To create a future where artificial intelligence is a ubiquitous part of everyday life.
No matter how well they are coded, machine learning models are only as good as the data that they are trained on. Large specialised enterprises such as Tesla and Cruise are able to develop in-house solutions to curate machine learning data sets, but this capability is beyond smaller organisations. Aquarium exists to level the playing field with a platform for collaborative machine learning projects that can highlight outliers and provide solutions for machine learning teams to action.
While a number of startups exist providing tools that improve machine learning code, Aquarium's strength is that it is one of the only companies focusing on improving the actual dataset, freeing machine learning engineers to focus on code rather than managing data. This solution helps improve model accuracy and reduce data gathering costs, and has been received enthusiastically by machine learning teams in companies such as Pinterest, AMP Robotics and Woven Planet.
Aquarium has completed a seed funding round led by Sequoia. It has used this investment to build out its core team and develop its product, moving from it from a closed beta to launch status. It is continuing to seek engineering talent to further improve its platform, in a bid to one day (in the words of its founders) do for machine learning what Figma did for design teams.
Freddie
Company Specialist at Welcome to the Jungle
Feb 2021
$2.6m
SEED
Aug 2020
$0.2m
SEED
This company has top investors
Peter Gao
(CEO)Was a Staff Software Engineer and Engineering Manager with Cruise Automation. Before that was a Teaching Assistant and Undegraduate Researcher at UC Berkeley.
Quinn Johnson
(Head of Engineering)Was Engineering Manager at Ouster and Cruise Automation, and a Software Engineer with Graphistry. Previously was an Internal Tools Engineer Intern at Palantir Technologies.