Case Study: Yotpo achieves sub-50ms real-time inference with Qwak

Yotpo cuts real-time inference to under 50ms with Qwak

Yotpo, a leader in ecommerce marketing, faced challenges scaling its AI-powered recommendation engine. Their data science team required intensive engineering support and a robust infrastructure to serve billions of users. They needed a flexible, real-time ML platform that would reduce operational overhead and allow for autonomous model management without building their own system. They chose to implement the Qwak MLOps platform to address these needs.

Qwak provided a seamless solution with its end-to-end platform. Integration was straightforward, requiring minimal code to deploy models. Qwak offered full visibility through its console, scalable real-time and batch deployment, and autonomous feature management via its Feature Store. As a result, Yotpo achieved operational efficiency, delivering millions of recommendations with zero engineering dependency. They notably reduced real-time inference to less than 50ms and had five new models running in production within four weeks.

Yotpo