Kaskada Announces General Availability of Its Feature Engineering Platform
- Cutting Edge Data Infrastructure Empowers Data Science Teams to Deliver Impactful Machine Learning With Event-Based Data
Kaskada, a machine learning company that empowers data scientists to build and operate machine learning solutions, announced the general availability of its feature engineering platform. This launch signifies that, after a period of beta testing with early adopters, the platform is ready for data science teams to use for 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, while 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.
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Kaskada’s feature engineering platform is the first 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.
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“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.”
“Unlike many data products, Kaskada is available to individual data scientists and companies alike. It is free for many scenarios and requires no setup,” added Bonaci. “We invite data scientists with fraud, dynamic pricing, personalization and similar event-based use cases to sign up, onboard, and join our growing data science community.”
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