ScyllaDB Powers Low-Latency, Scalable Online Feature Stores for Real-Time ML

This story was originally published on HackerNoon at: https://hackernoon.com/scylladb-powers-low-latency-scalable-online-feature-stores-for-real-time-ml . Discover how ScyllaDB enables fast, scalable online feature stores, integrating with Feast to deliver low-latency, high-throughput ML predictions. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning . You can also check exclusive content about #scylladb-feature-store , #online-feature-store , #real-time-ml-inference , #low-latency-database , #feast-integration , #machine-learning-features , #high-throughput-nosql , #good-company , and more. This story was written by: @scylladb . Learn more about this writer by checking @scylladb's about page, and for more stories, please visit hackernoon.com . Feature stores centralize ML features for training and inference, but real-time workloads demand ultra-low latency. ScyllaDB’s shard-per-core design and Cassandra/DynamoDB compatibility make it an ideal online store, supporting single-digit ms queries and petabyte-scale throughput. With Feast integration, teams can avoid vendor lock-in and build scalable, real-time ML apps.