As real-time AI/ML based use cases are infused in every facet of our lives, it’s becoming increasingly challenging to deliver ML-based use cases using real-time data with low latency. Built to handle high throughput, low-latency scoring for large datasets, Redis Enterprise is the answer for scalable and affordable online feature store that enables real-time feature serving at scale.
A large and growing portion of ML use cases rely on their online feature stores for online prediction serving while at the same time consuming fresh features. Serving these features with low latency consistently and reliably is very challenging. Doing this at scale is a challenge that traditional databases cannot meet.
ML-based use cases delivered in real-time using fresh live data directly impacts customer experience or improves business outcomes. However, reliably delivering these predictions online as the user interacts with the app, while at the same time consuming real-time features from streaming sources is very challenging.
With the rise of digital transformation, ML-based applications rely on huge and ever growing datasets with hundreds to thousands of features feeding ML systems at a massive scale. This adds more complexity and cost to the task of consistently and affordably serving features in real-time.
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