  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.
