Data is a mess without labels. Especially the types of data we humans churn out in our day-to-day language.
That's where Austin-based Alegion hopes to provide a shortcut by accurately labeling data and training data sets by using good old fashion humans -- as well as a machine learning platform.
“Just as assembly lines incorporate power tools and robotics to enable scale, ML model development will require machines training machines to achieve the highest levels of model confidence,” Nathaniel Gates, CEO and founder of Alegion, said in a statement. “Our customers can first leverage human judgement to train their model and then watch as newly trained machines are incorporated that allow unprecedented scaling.”
The startup, which is based in Austin and has an office in Malaysia, has counted Charles Schwab, Conde Nast and the State of Texas among its clients. And it's about to start a new growth spurt, backed by a $12 million Series A-2 funding round led by San Francisco-based RHS Investments, which also backed its $3.6 million Series A in 2017.
It plans to use the new funding to add active learning and other new technologies to its labeling platform. The company has about 75 employees currently.
