论文标题
密涅瓦：传统企业SaaS应用程序的便携式机器学习微服务框架
Minerva: A Portable Machine Learning Microservice Framework for Traditional Enterprise SaaS Applications
论文作者
论文摘要
在传统的SaaS企业应用程序中，微服务是成功部署机器学习（ML）模型的重要组成部分。通常，微服务会在软件服务设计，开发和交付方面提高效率。随着它们在整体软件的重新设计中变得无处不在，随着机器学习的添加，传统应用程序也变得越来越聪明。在这里，我们提出了一种便携式ML微服务框架Minerva（用于应用ML的微服务容器），作为在传统的传统传统SaaS应用程序套件中模块化和部署智能微服务套件的有效方法，尤其是在企业域中。我们确定并讨论将ML微服务纳入此类应用程序的需求，挑战和架构。 Minervas设计用于使用微服务架构的旧应用程序的最佳集成，利用轻型基础架构加速在此类应用程序中部署ML模型。
In traditional SaaS enterprise applications, microservices are an essential ingredient to deploy machine learning (ML) models successfully. In general, microservices result in efficiencies in software service design, development, and delivery. As they become ubiquitous in the redesign of monolithic software, with the addition of machine learning, the traditional applications are also becoming increasingly intelligent. Here, we propose a portable ML microservice framework Minerva (microservices container for applied ML) as an efficient way to modularize and deploy intelligent microservices in traditional legacy SaaS applications suite, especially in the enterprise domain. We identify and discuss the needs, challenges and architecture to incorporate ML microservices in such applications. Minervas design for optimal integration with legacy applications using microservices architecture leveraging lightweight infrastructure accelerates deploying ML models in such applications.
