Kaskada Brings New Method of Time Travel to Feature Engineering The unique approach brings major impact to businesses and the data science community
Seattle, WA
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October 27, 2021 09:00 AM Pacific Daylight Time
Kaskada today announced the broad availability of the first-ever feature engine with time travel. The company’s approach to this methodology is vastly different from competitors, and current customers are already benefiting from improved data models, reduced risk of leakage, and significant time savings.
“The foundation of Kaskada was built on the intention to have a positive impact on the data science community, and our feature engine with time travel will do just that,” said Kaskada CEO Davor Bonaci. “This is the tool many of us in the industry have been dreaming about.”
For those in the data science field, it’s not uncommon for it to take several months to collect data and have it be outdated or even incorrect by the time it’s ready for business application. Kaskada solves this problem by letting you compute what you need directly from event-based data for feature engineering, meaning significant time and money savings for any kind of business. This method also won’t delay business value because it reveals accurate data results from the very beginning.
“Where it used to take months just to get a single machine learning model in place, Kaskada makes it possible for data scientists to skip the guesswork and travel back in time to instantly understand and compute features relevant to a specific, current business problem,” explained Dr. Charna Parkey, Kaskada’s VP of Product. “It not only removes a huge point of friction, but it can have a significant impact on a company’s bottom line.”
Kaskada’s feature engine with time travel is available now. To learn more and try it out, please go to https://kaskada.com/ and sign up today.
About Kaskada:
Kaskada is an innovative, Seattle-based, machine learning company that is leveling up the data science and machine learning industries. We are the company that first solved temporal streaming joins, enabling running predictive models from event-based data. Using Kaskada enables customers to get more value from event-based data. Now, users can build models that weren't previously possible, that will actually work once put in production–without leakage.
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Forrest Carman
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