Kaskada Launches its Feature Engineering Platform

Kaskada, a machine learning company, is releasing its feature engineering platform, enabling data science teams to use the solution for a wide variety of use cases, including fraud, personalization, and recommendation engines.

Kaskada’s feature engineering platform is an ML platform for data scientists that focuses on the feature engineering and feature serving experience.

The platform includes a collaborative interface for data scientists and is powered by proprietary data infrastructure for computing across event-based data and serving features in production.

“Kaskada’s feature engineering platform is designed to make truly hard data problems in machine learning easy,” said Davor Bonaci, Kaskada co-founder and CEO. “Data science teams can now work better together, build better features and deliver results at a whole new level. I cannot wait to see what kind of impact they’ll accomplish in the months and years to come.”

Some of the most impactful machine learning models use real-time, event-based data, which provides valuable insights on how behavior changes over time. This data type is one of the most difficult to handle because of the lack of efficient data infrastructure needed to calculate features at arbitrary points in time and to deliver such features to both training and production environments.

“The biggest obstacle for data scientists today isn’t building the fanciest models,” said Max Boyd, data science lead at Kaskada. “It is the inability of current data platforms to bridge the gap between training and production, particularly with the computation of features derived from event-based data. In past roles, we struggled to use event-based data to its full potential because of infrastructure limitations and spent a lot of time hacking around the problem for minimal gains. Kaskada is a game changer for building and operating quality machine learning models with event-based data.”

The Kaskada Feature Engineering Platform is now available. The platform is free to start and data scientists have the option to pay to add additional users, manage more data, and access additional features.

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