Minerva: A Portable Machine Learning Microservice Framework for Traditional Enterprise SaaS Applications
This work addresses the problem of making enterprise SaaS applications more intelligent through ML microservices, but it appears incremental as it builds on existing microservice architectures without introducing a fundamentally new paradigm.
The paper tackles the challenge of integrating machine learning into traditional enterprise SaaS applications by proposing Minerva, a portable microservice framework that modularizes and deploys ML models efficiently, accelerating their deployment in legacy systems.
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.