MIT-birthed machine learning startup launches to automate data science

MIT-birthed machine learning startup launches to automate data science

Market news |
By Rich Pell

Founded out of the computer science and AI lab at MIT – CSAIL, Feature Labs claims to offer the industry’s first software solution that automates feature engineering – the process of using domain knowledge of the data to create features that make machine learning algorithms work – for machine learning and artificial intelligence (AI) applications.

The company’s software combines 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. In addition, the company says, it allows customers to rapidly iterate on prediction problems to achieve business goals.

“Data science is the fastest growing area in the technology industry,” says Chip Hazard, a general partner at Flybridge Capital Partners, which led the seed funding. “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.”

“With software tools that can automatically identify predictive patterns in data more than ten 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,” he says.

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. According to the company, “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.”

The structured process, says the company, 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.

In addition to Flybridge Capital Partners, participants in the funding included First Star Ventures and 122 West Ventures. Chip Hazard of 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

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