Sketching Feature Stores Reduce Memory And Latency

This article demonstrates using bloom-filter-backed sketching feature stores to compress and serve historical categorical features for recommender ML. Using a real click-prediction dataset (5.7 million examples, 1.762 billion extracted data points) and logistic regression, the compressed store achieved similar accuracy and throughput while fitting state in-process, reducing external lookups and operational costs.