Streaming Aggregation - The Spark Behind Real Time ML
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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?)
Next Event
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?)
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