Abstract
Machine learning is used in enterprise systems for automation, but it can be difficult to integrate many machine learning dependencies and utilities, due to the absence of a common architecture. In certain cases, machine learning frameworks are advanced for systems that are struggling for particular business area usage cases, resulting in redundant effort and incredible reuse. Acumos is a free and open-source framework. Acumos can conveniently bundle machine learning models into scalable containerized microservices that can be shared through the platform's catalogue and implemented into a range of business applications. Via Acumos app, providing a case study of packaging sentiment analysis and classification machine learning models, enabling allocation with others. We show that when adapting machine learning models to business applications, the Acumos framework reduces the technological burden on application developers.
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