  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.
