Feature Stores for Real-time AI/ML: Benchmarks, Architectures, and Case Studies

Real-time artificial intelligence (AI) and machine learning (ML) use cases are becoming increasingly popular, with feature stores playing a crucial role in their successful deployment to production. 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. Companies often perform thorough benchmarking to determine which choice of architecture or online feature store is the most performant and cost-effective. Feature stores such as Feast, Wix, Tecton, and Qwak have different architectures and support various types of features sources. The choice of an online feature store and its architecture can significantly impact performance and cost-effectiveness. Companies should carefully consider these factors when choosing a feature store for their real-time AI/ML use cases.

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