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.

翻译：机器学习任务的相关特征可能以一条或多条连续数据流的形式到达。在数据流上服务机器学习模型会带来数据路由、时间同步和速率控制等方面的系统挑战。本文提出EdgeServe，一种能够实时提供机器学习模型预测的分布式流式系统。我们在三个流式预测任务上评估了EdgeServe：（1）人体活动识别，（2）自动驾驶，以及（3）网络入侵检测。