# Parallelizing Machine Learning as a Service for the End-User

arXiv: https://arxiv.org/abs/2005.14080 · HTML: https://mlsanity.com/paper/2005.14080/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 3 of 3)

- [Process discovery on deviant traces and other stranger things](https://mlsanity.com/paper/2109.14883/) (2021)
- [Responsive parallelized architecture for deploying deep learning models in production environments](https://mlsanity.com/paper/2112.08933/) (2021)
- [Modelling of Received Signals in Molecular Communication Systems based machine learning: Comparison of azure machine learning and Python tools](https://mlsanity.com/paper/2112.10214/) (2021)

## Abstract (arXiv)

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.