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:
Tech Lead, Feature Store
Software engineer, Feature Store
"From the get go, it was clear that Qwak understand our needs and requirements. The simplicity of the implementation was impressive.
Automatic deployment and continuous training were crucial to allow us to scale. Qwak gave us a type of "Jenkins" for machine learning."
"Using Qwak allowed us to focus on creating a business impact rather than spending valuable time on our infrastructure setup.
At JLL our development is very time sensitive. As a result of implementing Qwak, we improved our execution time by 4.5X."
"With Qwak we were able to improve our ML delivery dramatically.
Qwak has allowed us to work to the highest engineering standards from day one and to invest the majority of our efforts in our business challenges and not into plumbing."