Seattle startup Kaskada, a tech company that provides a platform for data scientists and engineers to better collaborate in developing machine-learning algorithms, has successfully completed an $8 million funding round, bringing the total raised by the company to date to $9.8 million.
Kaskada plans to use the funding to fuel the company’s growth, expand its software engineering team and to deliver on customer demand. Investors in the current Series A funding round include Voyager Capital, NextGen Venture Partners, Founders’ Co-op and Walnut Street Capital Fund.
The company, founded by Chief Executive Officer Davor Bonaci and Chief Technology Officer Ben Chambers, both former Google Cloud employees, provides a collaborative platform for so-called feature engineering and serving. Features are key variables that are part of machine-learning algorithms. Kaskada’s platform allows data scientists and engineers to share features across the platform, increasing the efficiency of the data-engineering process.
“Deploying machine-learning features is a pain for data scientists,” Bonaci says. “Data scientists hand off features to data engineers who must reinvent the wheel to put them into production. Our platform enables feature stores, which let science and engineering teams share features across the organization.”
Kaskada is currently making its platform available for beta testing and expects to deliver its product to market in the first half of this year, according to the company. Kaskada, founded two years ago, previously raised $1.8 million from investors through a seed funding round in the fall of 2018.
“Kaskada is addressing a huge market need because nearly every company today is pouring significant resources into their data-science efforts and very few are seeing results that meet their expectations,” says James Newell, Voyager Capital Managing Director. “The Kaskada team’s prior work at Google Cloud … gave them the chance to see the need for a unified platform for feature engineering as well as the expertise to help companies accelerate and improve the delivery of machine-learning capabilities.”