- Labelbox recently announced the closing of a $110 million Series D funding. These are the details.
Labelbox — a leading training data platform for enterprise machine learning applications — recently announced the closing of a $110 million Series D funding led by SoftBank’s Vision Fund 2. And Snowpoint Ventures and Databricks Ventures also participated along with previous investors B Capital Group, Andreessen Horowitz and Catherine Wood, CEO and founder of ARK Invest. So far, Labelbox has raised $189 million in venture funding.
Labelbox’s software platform was designed to facilitate the entire training data iteration loop that improves ML model performance. And 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.
In order to build real-world applications, machine learning teams need robust infrastructure that can easily import raw data into labeling workflows, allow enterprises to manage widely distributed annotation teams, transparently monitor quality, adjust for bias, and export high-quality training data to machine learning models. And to deploy accurate models and drive optimal business outcomes, training data must be constantly improved. Additionally, 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.
Labelbox automates the process through a web-based platform that pre-labels data and allows enterprises to collaborate easily across databases, BPOs and labeling services regardless of time zone or geography. And 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.
The $110 million raised by Labelbox builds on its breakout success in providing Global 2000 customers with a full-cycle and iterative approach to machine learning, eliminating the bottleneck in getting AI into core products and services. And now enterprises are able to unlock the value in their proprietary data, allowing them in turn to differentiate their products and create new revenue with AI. Labelbox is also 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.
“Labelbox has become a complete AI training data platform for enterprises. Our customers use Labelbox as their data engine, leveraging active learning and facilitating human supervision to relentlessly improve AI model performance.”
— Manu Sharma, co-founder and CEO
“It’s not just about annotation. We cover this entire iteration loop on a single platform, continually optimizing the data with a focus on getting more and more efficient over time.”
— Brian Rieger, Labelbox co-founder and President
“The investment in Labelbox – the first as Databricks Ventures – felt like a natural fit given the strong existing partnership between our two companies. We started Databricks Ventures to support companies extending the lakehouse ecosystem and Labelbox’s collaborative training data platform allows companies to quickly produce structured data from unstructured data, and train AI on unstructured data on the Databricks Lakehouse. With this investment, we are looking forward to supporting Labelbox and our rapidly growing number of joint customers with streamlined, powerful capabilities.”
— Andrew Ferguson, Head of Databricks Ventures
“Data is the new oil and labelling is one of the most essential parts of the refinery. We believe that Labelbox has the most advanced end-to-end training data platform focused on collaboration, automation and data quality that simplifies the time-intensive process of data labelling, allowing technical resources to focus on performance and getting AI to production faster. We are delighted to partner with Manu Sharma and the team to support their mission to democratize access to AI development.”
— Robert Kaplan, Investment Director at SoftBank Investment Advisers
“Artificial intelligence training costs are falling dramatically every year and machine learning is transforming the economy. We believe the value of Labelbox is going to scale in line with the market as they have built a learning environment for AI that will help companies get far better at AI far faster.”
— Doug Philippone of Snowpoint Ventures