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Feature Labs, a startup with roots in research begun at MIT, officially launched today with a set of tools to help data scientists build machine learning algorithms more quickly.
Co-founder and CEO Max Kantor says the company has developed a way to automate “feature engineering,” which is often a time consuming and manual process for data scientists. “Feature Labs helps companies identify, implement, and most importantly deploy impactful machine learning products,” Kantor told TechCrunch.
Feature - Labs - Engineering - Process - Domain
He added, “Feature Labs is unique because we automate feature engineering, which is the process of using domain knowledge to extract new variables from raw data that make machine learning algorithms work.”
The company achieves this by using a process called “Deep Feature Synthesis,” which create features from raw relational and transactional datasets such as visits to the website or abandoned shopping cart items and automatically converts that into a predictive signal, Kantor explained.
Process - Error - Prone - Feature - Engineering
He says this is vastly different from current human-driven process, which is time-consuming and error prone. Automated feature engineering enables data scientists to create the same kinds of variables they would come up with on their own, but much faster without having to spend so much time on the underlying plumbing. “By giving data scientists this automated process, they can spend more time figuring out what they need to predict,” he said.
It achieved that in a couple of ways....
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