Labelbox is a training data platform that has been built to allow its users to improve their training data iteration loops. The platform is based on three blocks – Data Annotation, model performance Diagnosis, and based on obtained results Prioritization.
The Series D funding round was led by SoftBank’s Vision Fund II. Among new investors were Snowpoint Ventures and Databricks Ventures. Andreessen Horowitz, Eduardo Saverin’s B Capital Group, and the Ark Invest asset manager Cathie Wood also took part in the round. With the fundings raised the company became a unicorn.
Labelbox, headquartered in San Francisco, was founded by Brian Rieger, Daniel Rasmuson, Manu Sharma in 2017. Labelbox labels data as a part of the machine learning process. It enables smaller batches of training data to be annotated, and then AI models learn how to make the predictions and develop insights. “We are selling to the large organizations who have multiple AI use cases and who fundamentally believe that their differentiation in the future has to be done with AI, or else they’re going to become irrelevant”, Manu Sharma says. As of now, the company has more than 200 customers.
