BOSTON--(BUSINESS WIRE)--Feature Labs, Inc., the industry’s first software solution that automates feature engineering for machine learning and AI applications, today launched and announced a $1.5 million seed round of funding that was led by Flybridge Capital Partners and included participation from First Star Ventures & 122 West Ventures. Chip Hazard, a general partner at Flybridge, has joined the company’s Board of Directors and software industry veteran John Donnelly III has joined the company as Chief Operating Officer.
Feature Labs, Inc. was founded out of the Computer Science and AI Lab at MIT - CSAIL by Max Kanter, Kalyan Veeramachaneni and Ben Schreck. The company’s software products combine open source and proprietary algorithms that enable data scientists to perform automated and precise feature engineering on structured data so that machine learning and predictive modeling can be leveraged at scale and allows customers to rapidly iterate on prediction problems to achieve business goals. Organizations including Kohl’s, NASA, BBVA, DARPA, Monsanto and MIT have been using Feature Labs software to automate their feature engineering processes.
“Data science is the fastest growing area in the technology industry. Every industry leading enterprise company is trying to deploy machine learning applications into production, however all are facing the challenge of a shortage of data scientists to do the work,” said Hazard. “With software tools that can automatically identify predictive patterns in data more than 10 times faster than traditional approaches and a rock star founding team, Feature Labs is well positioned to fundamentally change the way organizations approach using their data.”
Today the process of feature engineering is manual, time consuming, error-prone and frequently does not work. Current methods do not allow for newcomer data scientists & domain experts to make an impact. The best way for machine learning and predictive models to enable business value is to automate this critical step.
According to Gartner analyst Dr. Carlton Sapp, “Machine Learning enterprise applications will require a strong automated feature engineering process in order to deploy effective predictive models.”
Feature Labs uses “Deep Feature Synthesis” to automatically create features from raw relational and transactional datasets such as user behavior logs or credit card transactions. The software creates complex features similar to humans by stacking primitive feature functions using the relationships between rows and tables in a dataset. As it explores a dataset, it updates the recommendations of the top features to use for specific prediction problems.
This structured process augments the human expertise required for effective machine learning after any required “data cleaning” has been performed. As a result, models can be confidently deployed without changing more feature engineering processes between development and production.
“We are thrilled to be working with investors who have a deep understanding of the enterprise software and machine learning markets,” said Max Kanter, CEO of Feature Labs. “I am also thrilled to welcome John to the team to focus on growing our go to market and commercial operations.”
About Feature Labs, Inc.
Feature Labs builds tools and API’s to deploy impactful machine learning solutions by combining open source software and proprietary algorithms for automated feature engineering. Founded in 2015 out of the Computer Science and AI Lab at MIT - CSAIL, the company is based in Boston, MA. For more information, visit www.featurelabs.com