EasyMLServe: Easy Deployment of RESTMachine Learning Services

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, i. e., energy time-series forecasting and cell instance
segmentation. The EasyMLServe framework and the use cases are available
on GitHub.