Feature Stores for Real-time AI & Machine Learning - KDnuggets

Real-time AI/ML use cases such as fraud prevention and recommendations are on the rise, and feature stores play a key role in deploying them successfully to production. According to popular open source feature store Feast, one of the most common questions users ask in their community Slack is: how scalable / performant is Feast? This is because the most important characteristic of a feature store for real-time AI/ML is the feature serving speed from the online store to the ML model for online predictions or scoring. Successful feature stores can meet stringent latency requirements (measured in milliseconds), consistently (think p99) and at scale (up to 100Ks of queries per second and even million of queries per second, and with gigabytes to terabytes sized datasets) while at the same time maintaining a low total cost of ownership and high accuracy. As we will see in this post, the choice of online feature store as well as the architecture of the feature store play important roles in determining how performant and cost effective it is.