Labelbox, a leading training data platform for enterprise machine learning applications, has announced the close of a USD 40 million Series C funding led by B Capital Group. First Round Capital, Andreessen Horowitz, Gradient Ventures (Google’s AI venture fund), and Kleiner Perkins are amongst the previous investors who participated, along with Catherine Wood, CEO and Founder of ARK Invest. So far, Labelbox has raised USD 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 built a software platform that could manage, annotate, and iterate training data, as it is amongst the most important intellectual properties of the artificial intelligence age. The data labeling market, which is currently a 4 billion dollar market, is expected to grow 4x times in the next four years. Availability of free and computing resources increases commoditized, high-quality labeled training data as it is the most valuable asset for those enterprises that adopt 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.”
For building real-world applications, machine learning teams use robust infrastructure to import raw data into labeling workflows. It allows enterprises to manage
widely distributed annotation teams, adjust for bias, monitor quality, and export high-quality labeled training data to machine-learning models.
For enterprise data, Labelbox functions like a command center. It automates the process with a web-based pre-labeling platform so that businesses can securely communicate and collaborate regardless of time zone or geography through databases, BPOs, and labeling services. Labelbox customers have experienced accelerating iteration cycles by up to 800% and halving development time by moving new models into production using the platform.
“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 Prieto-Moreno, Director, Corporate Technology Advanced Projects at FLIR. “We look forward to continuing to roll out the software across more enterprise initiatives.”