Minerva: A Portable Machine Learning Microservice Framework for Traditional Enterprise SaaS Applications


Venkata Duvvuri, Oracle Corp & Purdue University, USA


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. Minerva’s design for optimal integration with legacy applications using microservices architecture leveraging lightweight infrastructure accelerates deploying ML models in such applications.


Microservices, Enterprise SaaS applications, Machine Learning, Oracle Cloud Infrastructure, Docker

Full Text  Volume 10, Number 4