  The rat race between user-generated data and data-processing systems is
currently won by data. The increased use of machine learning leads to further
increase in processing requirements, while data volume keeps growing. To win
the race, machine learning needs to be applied to the data as it goes through
the network. In-network classification of data can reduce the load on servers,
reduce response time and increase scalability. In this paper, we introduce
IIsy, implementing machine learning classification models in a hybrid fashion
using off-the-shelf network devices. IIsy targets three main challenges of
in-network classification: (i) mapping classification models to network devices
(ii) extracting the required features and (iii) addressing resource and
functionality constraints. IIsy supports a range of traditional and ensemble
machine learning models, scaling independently of the number of stages in a
switch pipeline. Moreover, we demonstrate the use of IIsy for hybrid
classification, where a small model is implemented on a switch and a large
model at the backend, achieving near optimal classification results, while
significantly reducing latency and load on the servers.
