Decoupling Inference from State Updates in Low-Latency Feature Engines via Probabilistic Thinning
Streaming data systems increasingly underpin Machine Learning workflows that maintain large numbers of continuously updated aggregations. In production settings, each incoming event typically triggers read-modify-write operations to persistent storage, making high-frequency state updates a dominant source of latency, contention, and operational cost. In this work, we decouple inference from state persistence in streaming Machine Learning pipelines via probabilistic thinning: every event is scored, but durable state updates are selectively triggered by informative events. Unlike approaches that shed input or state, we show that persistence-path control is achievable without a high-frequenc...
| Authors | Augusto Peres, Iker Perez, Pedro Valdeira et al. |
|---|---|
| Primary category | cs.DB |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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