Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform
This addresses the problem of duplicated effort and lack of reuse for business application developers, though it is incremental as it builds on existing containerization and sharing concepts.
The paper tackles the difficulty of integrating diverse machine learning dependencies and services in business applications by introducing Acumos, an open platform that packages ML models into portable containerized microservices, reducing technical burden and enabling reuse across domains.
Applying Machine Learning (ML) to business applications for automation usually faces difficulties when integrating diverse ML dependencies and services, mainly because of the lack of a common ML framework. In most cases, the ML models are developed for applications which are targeted for specific business domain use cases, leading to duplicated effort, and making reuse impossible. This paper presents Acumos, an open platform capable of packaging ML models into portable containerized microservices which can be easily shared via the platform's catalog, and can be integrated into various business applications. We present a case study of packaging sentiment analysis and classification ML models via the Acumos platform, permitting easy sharing with others. We demonstrate that the Acumos platform reduces the technical burden on application developers when applying machine learning models to their business applications. Furthermore, the platform allows the reuse of readily available ML microservices in various business domains.