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Probabilistic Thinning Decouples ML Inference from State Updates

Researchers have developed a novel method called probabilistic thinning to decouple inference from state updates in low-latency feature engines for machine learning. This technique selectively triggers durable state updates only for informative events, rather than processing every incoming event. The approach aims to reduce latency, contention, and operational costs in streaming ML pipelines by controlling the persistence path without requiring a high-frequency in-memory control plane or cross-worker coordination. Evaluations show that up to 90% of events can be excluded from the persistence path while maintaining or improving downstream utility. AI

IMPACT Reduces latency and operational costs in streaming ML pipelines by optimizing state updates.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new method for machine learning systems.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Augusto Peres, Iker Perez, Pedro Valdeira, Guilherme Jardim, Ana Sofia Gomes, Hugo Ferreira, Pedro Bizarro ·

    Decoupling Inference from State Updates in Low-Latency Feature Engines via Probabilistic Thinning

    arXiv:2606.16981v1 Announce Type: cross Abstract: 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 …

  2. arXiv cs.LG TIER_1 English(EN) · Pedro Bizarro ·

    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…