arXiv:2303.08028
EdgeServe: A Streaming System for Decentralized Model Serving
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition Network
Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural Networks
Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic Dataset
DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition
Label Leakage in Federated Inertial-based Human Activity Recognition
A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT Systems
토론
이 논문과 연결된 포럼 글이 아직 없습니다.