EdgeServe: A Streaming System for Decentralized Model Serving
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Abstract (arXiv)
The relevant features for a machine learning task may arrive as one or more continuous streams of data. Serving machine learning models over streams of data creates a number of interesting systems challenges in managing data routing, time-synchronization, and rate control. This paper presents EdgeServe, a distributed streaming system that can serve predictions from machine learning models in real time. We evaluate EdgeServe on three streaming prediction tasks: (1) human activity recognition, (2) autonomous driving, and (3) network intrusion detection.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 13 of its 62 references that are in the map. First posted on arXiv 2 Mar 2023.
Builds on
13 of 62 references.
References · 13
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