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 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 trigg
The rapid expansion of AI applications and streaming data systems necessitates more efficient and cost-effective ways to manage real-time state updates, which are becoming a bottleneck.
This development addresses a critical performance and cost challenge in real-time AI and machine learning, enabling more scalable and responsive operational AI systems.
Machine learning pipelines can now potentially decouple inference from costly state persistence, leading to lower latency, reduced infrastructure costs, and improved system resilience.
- · AI/ML developers
- · Cloud service providers
- · High-frequency data platforms
- · Real-time analytics companies
- · Legacy database systems
- · Undifferentiated high-latency streaming solutions
Reduced operational overhead and improved performance for AI systems relying on continually updated aggregations.
Acceleration of new real-time AI applications across various industries due to lower infrastructure requirements and faster response times.
Enhanced competitive advantage for companies adopting this optimization, potentially leading to market consolidation or the emergence of new leaders in real-time AI services.
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Read at arXiv cs.LG