SoftBank’s Vision Fund 2 has led a $110 million funding round of Labelbox, the leading platform for training data for enterprise machine learning (ML) applications. Snowpoint Ventures and Databricks Ventures also participated, as did previous investors ARK Invest, Andreessen Horowitz, and B Capital Group. The company has currently raised $189 million in venture capital funding.

Manu Sharma, co-founder, and CEO said, “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.”

ML models perform better when Labelbox’s software platform facilitates the iteration loop of training data. Data is annotated and trained using a collection of tools, errors are identified during error analysis, annotations that are incorrect or ambiguous are refined and additional data is collected to supplement data, then the model is tested, and the error analysis is repeated in a continuous loop to improve model performance.

Brian Rieger, Labelbox co-founder, and President said, “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.”

For machine learning teams to build real-world applications, they need robust infrastructure that makes it easy for them to import raw data into labeling workflows, lets them manage widely distributed annotation teams, monitors quality transparently, counteracts bias, and exports high-quality training data to machine learning models. Training data must be improved continuously in order to deploy accurate models and achieve optimal business outcomes. Boost gives Labelbox customers access to a world-class workforce and dedicated labeling expertise that will help them succeed quickly on the platform and ultimately become more efficient and effective with time.

Labelbox raised $110 million to build upon its breakthrough success enabling Global 2000 customers to build AI-enabled products and services via a full-cycle, iterative approach to machine learning. Now that companies are able to unlock the value in their proprietary data, they can differentiate their products and create new revenue streams. The Labelbox platform is currently used by customers that include ArcelorMittal, Chegg, Genentech, and Warner Brothers. It is used in industries including agriculture, insurance, health care, media, and military intelligence.