Softbank Vision Fund Leads $110 Million Round in Labelbox

<p align&equals;"left"><strong>SAN FRANCISCO<&sol;strong> &&num;8212&semi; Labelbox&comma; a training data platform for enterprise machine learning applications&comma; has closed a &dollar;110 million Series D funding led by SoftBank’s Vision Fund 2&period; Snowpoint Ventures and Databricks Ventures also participated along with previous investors B Capital Group&comma; Andreessen Horowitz and Catherine Wood&comma; CEO and founder of ARK Invest&period; To date&comma; Labelbox has raised &dollar;189 million in venture funding&period;<&sol;p>&NewLine;<p>&OpenCurlyDoubleQuote;Labelbox has become a complete AI training data platform for enterprises&comma;” said Manu Sharma&comma; co-founder and CEO&period; &OpenCurlyDoubleQuote;Our customers use Labelbox as their data engine&comma; leveraging active learning and facilitating human supervision to relentlessly improve AI model performance&period;”<&sol;p>&NewLine;<p>Labelbox’s software platform is designed to facilitate the entire training data iteration loop that improves ML model performance&period; It integrates a collection of tools to annotate data and train AI models&comma; conduct error analysis to identify data on which the model performs poorly&comma; refine annotations found to be incorrect or ambiguous&comma; 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&period;<&sol;p>&NewLine;<p>&OpenCurlyDoubleQuote;It&&num;8217&semi;s not just about annotation&comma;” said Brian Rieger&comma; Labelbox co-founder and President&period; &OpenCurlyDoubleQuote;We cover this entire iteration loop on a single platform&comma; continually optimizing the data with a focus on getting more and more efficient over time&period;”<&sol;p>&NewLine;<p>To build real-world applications&comma; machine learning teams need robust infrastructure that can easily import raw data into labeling workflows&comma; allow enterprises to manage widely distributed annotation teams&comma; transparently monitor quality&comma; adjust for bias&comma; and export high-quality training data to machine learning models&period; To deploy accurate models and drive optimal business outcomes&comma; training data must be constantly improved&period; Additionally&comma; Labelbox offers Boost – a service that features a world-class workforce and dedicated labeling expertise to ensure customers find success quickly on the platform and then continually become more efficient and effective&period;<&sol;p>&NewLine;<p>&&num;8220&semi;The investment in Labelbox &&num;8211&semi; the first as Databricks Ventures &&num;8211&semi; felt like a natural fit given the strong existing partnership between our two companies&comma;&&num;8221&semi; said Andrew Ferguson&comma; Head of Databricks Ventures&period; &&num;8220&semi;We started Databricks Ventures to support companies extending the lakehouse ecosystem and Labelbox&&num;8217&semi;s collaborative training data platform allows companies to quickly produce structured data from unstructured data&comma; and train AI on unstructured data on the Databricks Lakehouse&period; With this investment&comma; we are looking forward to supporting Labelbox and our rapidly growing number of joint customers with streamlined&comma; powerful capabilities&period;”<&sol;p>&NewLine;<p>Labelbox automates the process with a web-based platform that pre-labels data and allows enterprises to collaborate easily across databases&comma; BPOs and labeling services regardless of time zone or geography&period; Labelbox customers report accelerating iteration cycles by up to 800 percent using the platform and cutting in half the time it takes to push new models into production&period;<&sol;p>&NewLine;<p>&OpenCurlyDoubleQuote;Data is the new oil and labelling is one of the most essential parts of the refinery&comma;” said Robert Kaplan&comma; Investment Director at SoftBank Investment Advisers&period; &OpenCurlyDoubleQuote;We believe that Labelbox has the most advanced end-to-end training data platform focused on collaboration&comma; automation and data quality that simplifies the time-intensive process of data labelling&comma; allowing technical resources to focus on performance and getting AI to production faster&period; We are delighted to partner with Manu Sharma and the team to support their mission to democratize access to AI development&period;”<&sol;p>&NewLine;