Labelbox, the leading training data platform for enterprise machine learning applications, today announced the close of a $40 million Series C funding led by B Capital Group. Previous investors Andreessen Horowitz, First Round Capital, Gradient Ventures (Google’s AI venture fund) and Kleiner Perkins also participated in addition to Catherine Wood, CEO and founder of ARK Invest. To date, Labelbox has raised $79 million in venture funding.
“We give ML teams a complete workflow that organizes and manages data, people and processes to drive competitive business advantage for companies in every major enterprise vertical,” said Manu Sharma, Labelbox’s CEO and co-founder. “Our software platform creates the best collaboration possible between automation and human expertise. We understand that our customer’s success depends on speed. Faster iteration loops via automation is the engine powering improved performance in AI.”
Labelbox offers a software platform to manage, annotate and iterate training data, the most important intellectual property of the artificial intelligence age. Data labeling is a 4 billion dollar market that is expected to nearly quadruple in the next four years. With algorithms available for free and computing resources increasingly commoditized, high-quality labeled training data is the most valuable asset for enterprises adopting supervised learning solutions.
“Data has become the most valuable asset a company can possess, regardless of industry, and machine learning is emerging as the key enabler for digital transformation by leveraging data insights. However, most enterprises that adopt machine learning spend over 80% of their time in data labeling and data management,” said Rashmi Gopinath, General Partner at B Capital Group.
“Labelbox’s training data platform supports many of the Fortune 500 enterprises and federal agencies with an unparalleled set of tools to unlock the full potential of machine learning and deploy accurate models that are constantly improving to help drive better business outcomes,” Gopinath continued. “We’re excited to partner with Manu and Brian and the entire Labelbox team to build a category leader in machine learning data infrastructure and enable enterprises to realize meaningful ROI from their AI/ML investments.”
To build real-world applications, machine learning teams need robust infrastructure that is able to import raw data into labeling workflows, allowing enterprises to manage widely distributed annotation teams, monitor quality, adjust for bias, and export high-quality labeled training data to machine-learning models.
Labelbox functions like a command center for enterprise data. It automates the process with a pre-labeling web-based platform so that enterprises can connect and collaborate easily across databases, BPOs and labeling services regardless of time zone or geography. Labelbox customers report accelerating iteration cycles by up to 800 percent using the platform and cutting development time in half in pushing new models into production.
“Labelbox software provides the advanced annotation capabilities that our teams need for our diverse AI projects. The platform facilitates collaboration and management of multiple distributed labeling workforces, and the integration between our internal processes and the Labelbox platform is easy and works like a charm,” noted Andres Pretio-Moreno, Director, Corporate Technology Advanced Projects at FLIR. “We look forward to continuing to roll out the software across more enterprise initiatives.”
Labelbox is currently being used by industries as diverse as agriculture, insurance, healthcare, media, and military intelligence with customers that include Genentech, Warner Brothers, Bayer, and BASF.
Founded in 2018 and based in San Francisco, Labelbox is the world’s leading training data platform for machine learning applications. Rather than requiring companies to build their own expensive and incomplete homegrown tools, Labelbox created the world’s first collaborative training data platform that acts as a command center for data science teams to interface with dispersed annotation teams. Better ways to input and manage data leads to higher quality training data, more accurate machine-learning models and faster iterations to improve the algorithms that drive business growth. Labelbox has raised $79 million in capital from leading venture capital firms in Silicon Valley, including Andreessen Horowitz, B Capital Group, Gradient (Google’s AI venture firm), Kleiner Perkins and more. For more information, visit: https://www.Labelbox.com/
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