Streaming Aggregation - The Spark Behind Real Time ML

Real time ML tasks, such as credit card fraud detection, recommendation systems, anomaly detection and others use features that are computed as aggregates over real-time data streams.Data freshness and low latency are key in these use cases, yet achieving them often results in a resource-intensive solution and an angry CFO.Join Gal Lushi and Yoni Ben-Dayan as they take us through how Qwak’s engineering teams built a turn-key streaming aggregation solution that addresses challenges, while guaranteeing:

  • High data freshness, low latency and high throughput
  • EXACTLY ONCE and support for late arrivals.
  • Efficient handling of multiple small and long time windows.
  • Consistency between inference and training (because who wants a training-serving skew?)
Gal Lushi

Gal Lushi

Tech Lead, Feature Store

Yoni Bendayan

Yoni Bendayan

Software engineer, Feature Store

Streaming Aggregation - The Spark Behind Real Time ML

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Brand Leaders are Talking About Qwak

Oren Neiberg
Machine Learning Engineer
“With Qwak we were able to improve our ML delivery dramatically.”

Or Hiltch
VP of Engineering
“Using Qwak allowed us to focus on creating a business impact rather than spending valuable time on our infrastructure setup.”
Jonathan Yaniv
Data Science Leader
“We love Qwak because it provides a unified, end-to-end solution for managing ML-based applications in production.”