April 30, 2024Feature Stores for Real-time AI/ML: Benchmarks, Architectures, and Case Studies
Real-time artificial intelligence/machine learning (AI/ML) use cases, such as fraud detection and recommendation, 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 millions of queries per second, with gigabytes to terabytes-sized datasets) while at the same time maintaining a low total cost of ownership and high accuracy.
