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§ Private Profile · Boston, MA, USA
Software automates feature engineering, extracting variables from raw data to improve ML algorithms for data scientists.
Feature Labs is a Cambridge, Massachusetts-based software company that develops automated feature engineering tools to help data scientists and business analysts extract new variables from raw data for machine learning algorithms. Prior to its strategic acquisition, the enterprise software startup raised $3 million in total venture capital funding, which included a $1.5 million seed round, to commercialize its core technology. The organization also distributed open-source software libraries that achieved widespread adoption within the global data science community, accumulating over 350,000 total downloads. Originating from academic research conducted at the Massachusetts Institute of Technology, the business was ultimately acquired in October 2019 by the data analytics platform Alteryx, led by chief executive officer Dean Stoecker. Feature Labs was originally founded in 2015 by the team of Max Kanter, Kalyan Veeramachaneni, and Ben Schreck.
Feature Labs has raised $2.0M across 1 funding round.
Feature Labs has raised $2.0M in total across 1 funding round.
Feature Labs has raised $2.0M in total across 1 funding round.
Feature Labs's investors include Flybridge, First Star Ventures, Gotham Gal Ventures, Grace Beauty Capital, Monarch Collective, Pitbull Ventures, Susa Ventures, Karl Jacob, 122 West Ventures.
Feature Labs has raised $2.0M across 1 funding round. Most recently, it raised $2.0M Seed in February 2018.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Feb 1, 2018 | $2M Seed | Flybridge | First Star Ventures, Gotham GAL Ventures, Grace Beauty Capital, Monarch Collective, Pitbull Ventures, Susa Ventures, Karl Jacob, 122 WEST VENTURES | Announced |
Feature Labs was a Boston-based technology company that developed software for automating data science and machine learning workflows, particularly through its innovative Deep Feature Synthesis process and tools like Featuretools.[1][2][3] It targeted enterprises and data scientists struggling with the manual, time-intensive task of feature engineering—the critical step of transforming raw data into usable inputs for machine learning models—enabling faster development and deployment of intelligent products.[1][2][4] Serving clients like Accenture and international banks, Feature Labs addressed the skills gap in machine learning adoption by automating data preparation, model fitting, and hyper-parameter tuning, which boosted model accuracy and efficiency; the company raised $1.5M before its acquisition by Alteryx in October 2019.[1][3]
Post-acquisition, Feature Labs' technology integrated into Alteryx's platform to empower data workers worldwide with code-free and code-friendly advanced analytics, aligning with the booming demand for automated AI tools.[3][4]
Feature Labs emerged from research at MIT's Computer Science and Artificial Intelligence Lab (CSAIL), founded in 2015 by Max Kanter (CEO, with prior roles at Twitter, The New York Times, Fitbit, and Hewlett Packard) and Kalyan Veeramachaneni (MIT researcher).[1][2][3] Their breakthrough came from a 2015 paper, “Deep Feature Synthesis: Towards Automating Data Science Endeavors,” which automated the tedious feature engineering process and drew immediate industry interest.[2]
Ben Schreck, another MIT researcher, soon joined the team. Early traction was swift: Accenture became one of their first customers, using the software to build an AI-powered project manager from historical data.[2][4] This MIT spinout validated its tech through corporate sponsorships and DARPA funding via the D3M program, evolving from academic innovation to a subscription-based enterprise solution called Machine Learning 2.0—a seven-step framework for rapid model deployment.[2][4]
Feature Labs rode the early AI/ML automation wave in the mid-2010s, when enterprises faced exploding data volumes but lacked talent for manual feature engineering, a bottleneck in 80-90% of ML project time.[2][3][4] Its timing was ideal amid rising demand for operationalized ML beyond proofs-of-concept, fueled by market forces like the AI skills shortage (affecting 54 million data workers) and the shift to scalable, automated analytics platforms.[3]
By open-sourcing Featuretools and partnering with MIT courses, it democratized ML tools, influencing education and accelerating ecosystem adoption; the Alteryx acquisition amplified this, embedding automation into enterprise analytics and competing in the full-service AI space.[1][3][6]
Feature Labs' legacy endures through Alteryx (now integrated into broader analytics ecosystems), with its automation tech poised to evolve amid trends like generative AI, no-code ML, and edge computing, where feature engineering remains a chokepoint.[3][6] Expect expanded use in real-time applications like fraud detection and predictive services, as enterprises prioritize speed-to-value.
As AI shifts from experimentation to ubiquity, Feature Labs' foundational automation—once a startup innovator—continues smoothing paths for data-driven decisions, underscoring how targeted tools like Deep Feature Synthesis propel the tech sector forward.[1][2][4]