Minerva：面向传统企业SaaS应用的可移植机器学习微服务框架
Minerva: A Portable Machine Learning Microservice Framework for Traditional Enterprise SaaS Applications
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。一般来说，微服务可以提高软件服务设计、开发和交付的效率。随着它们在单片软件的重新设计中变得无处不在，随着机器学习的加入，传统的应用程序也变得越来越智能。在这里，我们提出了一个可移植的ML微服务框架Minerva（应用ML的微服务容器），作为在传统的传统SaaS应用程序套件中模块化和部署智能微服务的有效方法，特别是在企业领域。我们确定并讨论了将ML微服务纳入此类应用程序的需求，挑战和架构。Minervas设计使用微服务架构与传统应用程序进行最佳集成，利用轻量级基础设施加速在此类应用程序中部署ML模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
