Parallelizing Machine Learning as a Service for the End-User
This work addresses scalability issues for end-users of ML services, but it is incremental as it builds on existing distributed methods.
The paper tackles the challenge of scaling machine learning services for growing user bases by proposing a distributed architecture to parallelize ML pipelines, demonstrating significant computational gains through extensive experiments.
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.