  As ML applications are becoming ever more pervasive, fully-trained systems
are made increasingly available to a wide public, allowing end-users to submit
queries with their own data, and to efficiently retrieve results. With
increasingly sophisticated such services, a new challenge is how to scale up to
evergrowing user bases. In this paper, we present a distributed architecture
that could be exploited to parallelize a typical ML system pipeline. We propose
a case study consisting of a text mining service and discuss how the method can
be generalized to many similar applications. We demonstrate the significance of
the computational gain boosted by the distributed architecture by way of an
extensive experimental evaluation.
