EasyMLServe: Easy Deployment of REST Machine Learning Services
It addresses deployment challenges for scientific users, but it is incremental as it builds on existing REST frameworks.
The paper tackles the problem of deploying machine learning models by proposing EasyMLServe, a software framework for cloud-based REST services with generic GUIs, and demonstrates its application in energy forecasting and cell segmentation, with the framework available on GitHub.
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.