Real-time ML-based applications are on the rise but deploying them at scale for large datasets with low latency and high throughput is challenging. This talk will discuss the important role of feature stores for machine learning in deploying these applications.
By exploring a number of use cases in production, we will see how the choice of online data store and the feature store data architecture play important roles in determining its performance and cost. Throughout the presentation, we will illustrate key points by connecting them to juggling and Dr. Seuss! Stay tuned :)