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Feature stores are central data stores to power operational machine learning models. They help you store transformed feature values in a scalable and performant database. Real-time inference requires features to be returned to applications with low latency at scale. This is where ScyllaDB can play a crucial role in your machine learning infrastructure.
ScyllaDB is a real-time high-throughput NoSQL database that is best suited for feature stores where you require low latency consistently, and need peta-byte scalability.
Low-latency: ScyllaDB can provide <10 ms P99 latency. Low latency can speed up training time and leads to faster model development.
High-throughput: Training requires huge amounts of data and processing large datasets with many millions of operations per second - something that ScyllaDB excels at.
Large-scale: ScyllaDB can handle petabytes of data while still providing great performance.