EasyMLServe: Easy Deployment of REST Machine Learning Services

TL;DR

EasyMLServe simplifies deploying machine learning models as RESTful cloud services with GUIs, addressing specific needs of scientific users and demonstrated through real-world applications in energy forecasting and cell segmentation.

Contribution

We introduce EasyMLServe, a framework that facilitates cloud deployment of ML services with GUIs, tailored for scientific applications, filling gaps in existing REST-based solutions.

Findings

Framework supports deployment of ML services with GUIs.

Applied successfully to energy forecasting and cell segmentation.

Framework and use cases available on GitHub.

Abstract

Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and developers have to implement complete solutions which are often installed locally and include Graphical User Interfaces (GUIs). Distributing software to various users on-site has several problems. Therefore, we propose a concept to deploy software in the cloud. There are several frameworks available based on Representational State Transfer (REST) which can be used to implement cloud-based machine learning services. However, machine learning services for scientific users have special requirements that state-of-the-art REST frameworks do not cover completely. We contribute an EasyMLServe software framework to deploy machine learning services in the cloud using REST interfaces and generic local or web-based GUIs. Furthermore, we apply our framework on two real-world applications, \ie, energy time-series forecasting and cell instance segmentation. The EasyMLServe framework and the use cases are available on GitHub.