sc 23(3):

Research Article

Evaluation of Frameworks for MLOps and Microservices

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  • @ARTICLE{10.4108/eetsc.3661,
        author={Igor Urias and Rog\^{e}rio Rossi},
        title={Evaluation of Frameworks for MLOps and Microservices},
        journal={EAI Endorsed Transactions on Smart Cities},
        volume={7},
        number={3},
        publisher={EAI},
        journal_a={SC},
        year={2023},
        month={11},
        keywords={Machine Learning, Machine Learning Operations, Microservices},
        doi={10.4108/eetsc.3661}
    }
    
  • Igor Urias
    Rogério Rossi
    Year: 2023
    Evaluation of Frameworks for MLOps and Microservices
    SC
    EAI
    DOI: 10.4108/eetsc.3661
Igor Urias1,*, Rogério Rossi1
  • 1: Universidade de São Paulo
*Contact email: igor_bernardes.urias@hotmail.com

Abstract

Information Technology involves solutions for many kinds of industries and organizations, offering conditions for solving problems of different types and complexities. Artificial Intelligence, and more specifically applications that considers Machine Learning (ML) and Software Technology are part of these solutions for solving problems, including solutions for solving problems that involve smart cities approach. In order to present frameworks that deal with the operationalization of Machine Learning and Software technology, this article is based on the study and evaluation of frameworks that involve Machine Learning Operations (MLOps) and microservices. Specifically, three frameworks that integrate ML algorithms with microservices are evaluated based on a bibliographical review in scientific journals of relevance to the area. From an exploratory analysis of these frameworks, it was possible to highlight their main objectives, their benefits, and their ability to offer solutions that favor the large-scale use of Machine Learning algorithms in problem solving. The main results are highlighted in the article through a qualitative analysis that considers six evaluation criteria, such as: capacity for sharing resources, scope of use by users, and use in a cloud environment. The results achieved are satisfactory since the work allows, through a qualitative view of the evaluated frameworks, a perspective of how the integration of MLOps and microservices has been carried out, its benefits and possible results achieved through this integration.