Alberto Rizzoli
Simon Edwardsson
Machine learning-powered computer vision models are helping tackle a range of challenges facing society today, from spotting cancers to robotic farming. But when building an ML system, 80% of a team’s time is spent managing training data. This is a slow process that helps refine and augment the “knowledge” that models have learned by having humans perform laborious, manual labeling tasks.
V7 automates the labeling process, allowing companies to solve data labeling tasks ten times more quickly. The company’s unique “programmatic labeling” workflows use AI models and minimal human steering to apply labels to data at scale.
The next generation of software will not run on code, it will run on models fueled by training data. V7 helps clients turn human knowledge into AI models, improving the safety and accuracy of AI. If we want to build software that tackles unsolved frontiers in human health, or our climate, this is the only way.
