Labelbox nabs $10 million in Series A led by Google to accelerate industrial AI

Building great machine learning applications requires a huge amount of high quality labeled training data. Eighty percent of the time spent on developing machine learning is related to data management, which slows innovation and results in long design-build-test cycles. Machine learning workflows do not have standard tooling for labeling data, storing it, debugging models and then continually improving model accuracy. Enter Labelbox, a collaborative training data platform for machine learning applications, has raised $10 million Series A funding to continue building a comprehensive solution for machine learning teams to create and manage labeled training data for computer vision applications

Labelbox is building the best computer vision data labeling and management solution for industrial machine learning applications. Labelbox’s vision is to become the default software for machine learning teams to create and manage high-quality training data, in the same way, that GitHub is the default for software engineers.

Today the company announced it has raised $10 million Series A funding to continue building a comprehensive solution for machine learning teams to create and manage labeled training data for computer vision applications. The round was led by Gradient Ventures, Google’s AI-focused venture fund, with participation from previous investors Kleiner Perkins, First Round Capital, and angel investor, Sumon Sadhu.

In conjunction with the funding, Labelbox also announced that Anna Patterson, founder and Managing Partner at Gradient Ventures, VP of Engineering at Google, and Square board member, will join its board. The company also plans to use the funding to double its headcount in 2019 by hiring additional talent to fill engineering, sales, marketing, and customer success roles.

Founded just a year ago by Brian Rieger, Daniel Rasmuson, Manu Sharma, and Ysiad Ferreiras, the San Francisco, California-based Labelbox is a collaborative training data platform for computer vision machine learning applications. Rather than requiring companies to create their own expensive and incomplete homegrown tools, we’ve created the world’s first training data platform that acts as a central hub for humans to interface with AI. When humans have better ways to input and manage data, machines have better ways to learn. Labelbox has raised $14 million in capital from leading VCs in Silicon Valley.

“Labelbox substantially reduces model development times and empowers data science teams to build great machine learning applications. With the new funding, Labelbox will continue to double down on bringing data labeling infrastructure to the machine learning teams with powerful automation, collaboration, and enterprise-grade features. We’re excited to work with the team at Gradient Ventures and appreciate their support as we scale our business to meet customer demand,” said Manu Sharma, founder and CEO of Labelbox. “We’re also proud to have incredible investors who have believed in us since the beginning, such as Bucky, Ilya, and Bill from Kleiner Perkins and First Round Capital.”

Labelbox experienced significant growth in 2018. Customers include FLIR Systems, Lytx, Airbus, Genius Sports, KeepTruckin and thousands of users worldwide. Numerous machine learning teams use Labelbox to build and optimize models from aquaculture to autonomous driving.

“Labelbox is well-positioned to fuel the industrialization of machine learning across many sectors, such as manufacturing, transportation, and healthcare. In doing so, they will unlock the potential of AI for companies across the globe,” said Anna Patterson, Founder and Managing Partner at Gradient Ventures.

“The confluence of accessible GPU compute and deep learning technology has paved the way for companies to build production AI systems. The hard part now is teaching machines to think. Labelbox is the key technology to do just that, enabling AI teams to develop new breakthrough products,” said Peter Welinder, advisor to Labelbox and Research Scientist at Open AI.

LabelBox Team