Labelbox Raises $110M in Series D Funding

Labelbox, a San Francisco CA-based company training data platform for enterprise machine learning applications, raised $110M in Series D funding.

The round was led by SoftBank’s Vision Fund 2 with participation from Snowpoint Ventures and Databricks Ventures along with previous investors B Capital Group, Andreessen Horowitz and Catherine Wood, CEO and founder of ARK Invest. To date, Labelbox has raised $189m in funding.

The company intends to use the capital to accelerate growth and production.

Led by Manu Sharma, CEO, and Brian Rieger, co-founder and President Labelbox provides a training data platform for machine learning applications. Rather than requiring companies to build their own homegrown tools, the company created a collaborative platform that acts as a command center for data scientists to collaborate with dispersed annotation teams.

The solution is designed to facilitate the entire training data iteration loop that improves ML model performance. It integrates a collection of tools to annotate data and train AI models, conduct error analysis to identify data on which the model performs poorly, refine annotations found to be incorrect or ambiguous, supplement data through augmentation or additional data collection and then test the model and repeat the error analysis in a continuous loop that improves model performance.

Labelbox is currently being used by industries as diverse as agriculture, insurance, healthcare, media, and military intelligence with customers that include ArcelorMittal, Chegg, Genentech and Warner Brothers.