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Kaskada Releases Feature Engineering Platform

Data infrastructure enables delivery of machine learning with event-based data.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Kaskada, a machine learning company that enables data scientists to build and operate machine learning solutions, released its feature engineering platform. Data science teams can apply the platform to a wide variety of use cases, including fraud, personalization, and recommendation engines.

Machine learning is rapidly changing how companies do business and serve their customers. These opportunities, however, tend to be exploited most by large technology companies with significant resources invested in data collection, data processing, and productionization of machine learning. Others often struggle to achieve the same level of results. A key missing piece of getting to success is a data infrastructure that bridges the gap between model training and live serving of machine learning results in production environments.

Kaskada’s feature engineering platform is a 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.”

Some of the most impactful machine learning models use real-time, event-based data, which provides valuable insights into 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.

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