论文标题
Easymlserve：轻松部署REST机器学习服务
EasyMLServe: Easy Deployment of REST Machine Learning Services
论文作者
论文摘要
各种研究领域使用机器学习方法，因为它们可以通过从数据中学习来解决复杂的任务。但是，部署机器学习模型并不是微不足道的，开发人员必须实现通常在本地安装并包括图形用户界面（GUI）的完整解决方案。将软件分配给各种现场用户有几个问题。因此，我们提出一个概念将软件部署在云中。有几个基于代表性状态转移（REST）可用的框架，可用于实现基于云的机器学习服务。但是，针对科学用户的机器学习服务有特殊要求，即最先进的休息框架并不能完全涵盖。我们为使用REST界面和一般的本地或基于Web的GUIS贡献了EASYMLSEVER软件框架，以在云中部署机器学习服务。此外，我们将框架应用于两个现实世界应用，即\ ie，能源时间序列预测和单元格实例分割。 EASYMSLEVER框架和用例可在GitHub上找到。
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.
