Evaluation of Frameworks for MLOps and Microservices
Keywords:Machine Learning, Machine Learning Operations, Microservices
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.
El Naqa, I., Murphy, M. J. What is machine learning? In: Machine Learning in Radiation Oncology. Springer International Publishing. 2015. 3-11.
Shinde, P. P., Shah, S. A review of machine learning and deep learning applications. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) IEEE. 2018. 1-6. DOI: 10.1109/ICCUBEA.2018.8697857.
Silva, R., Silva, M., Caldas, G., Portela, F., Santos, H. Intelligent Dashboards to Monitor the Occurrences in Smart Cities–A Portuguese Case Study. EAI Endorsed Transactions on Smart Cities. 2022; 6(4):1-8. DOI: 10.4108/eetsc.v6i4.2796
Adamuscin, A., Golej, J., Panik, M. The challenge for the development of Smart City Concept in Bratislava based on examples of smart cities of Vienna and Amsterdam. EAI Endorsed Transactions on Smart Cities. 2016; 1(1):1-13.
Yousif, M., Microservices. IEEE Cloud Computing. 2016; 3(5):4-5. DOI: 10.1109/MCC.2016.101.
Larrucea, X. et al. Microservices. IEEE Software. 2018; 35(3), 96-100. DOI: 10.1109/MS.2018.2141030.
Riehle, D. Framework Design: A role modeling approach. 2000. (Doctoral dissertation). ETH Zurich.
Kreuzberger, D., Kuhl, N., Hirschl, S. Machine Learning Operations (MLOps): Overview, Definition, and Architecture. arXiv preprint arXiv:2205.02302, 2022.
Symeonidis, G., et al. MLOps-Definitions, Tools, and Challenges. In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) IEEE. 2022. 453-460. DOI: 10.1109/CCWC54503.2022.9720902.
Goyal, A. Machine Learning Operations. International Journal of Information Technology Insights & Transformations. 2020; 4(2).
Thones, J. Microservices. IEEE Software. 2015; 32(1): 116-116. DOI: 10.1109/MS.2015.11.
Newman, S. Building Microservices. O'Reilly Media Inc., 2021.
De Lauretis, L. From monolithic architecture to microservices architecture. In: 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). IEEE. 2019. 93-96. DOI: 10.1109/ISSREW.2019.00050.
Hassan, S., Bahsoon, R. Microservices and their design trade-offs: A self-adaptive roadmap. In: 2016 IEEE International Conference on Services Computing (SCC). IEEE. 2016. 813-818. DOI: 10.1109/SCC.2016.113.
Jamshidi, P. et al. Microservices: The journey so far and challenges ahead. IEEE Software. 2018; 35(3): 24-35. DOI: 10.1109/MS.2018.2141039.
Bass, L., Clements, P., Kazman, R. Software Architecture in practice. Boston: Addison-Wesley Professional Publishing, 2003.
Wang, H., Ma, C., Zhou, L. A brief review of Machine Learning and its application. In: 2009 International Conference on Information Engineering and Computer science. IEEE. 2009. 1-4. DOI: 10.1109/ICIECS.2009.5362936.
Prudius, A. A., Karpunin, A. A., Vlasov, A. I. Analysis of Machine Learning Methods to improve efficiency of Big Data processing in Industry 4.0. Journal of Physics: Conference Series. 2019; 1333(3):1-6. DOI: 10.1088/1742-6596/1333/3/032065.
Ayodele, T. O. Types of Machine Learning Algorithms. New Advances in Machine Learning. 2010; 3(1):19-48.
Mohri, M., Rostamizadeh, A., Talwalkar, A. Foundations of Machine Learning. MIT Press, 2018.
Leite, L. et al. A survey of DevOps concepts and challenges. ACM Computing Surveys (CSUR). 2019; 52(6): 1-35.
Wang, R. Y., Kon, H. B., Madnick, S. E. Data quality requirements analysis and modeling. In: Proceedings of IEEE 9th International Conference on Data Engineering. IEEE. 1993. 670-677. DOI: 10.1109/ICDE.1993.344012.
Dyck, A., Penners, R., Lichter, H. Towards definitions for release engineering and DevOps. In: 2015 IEEE/ACM 3rd International Workshop on Release Engineering. IEEE. 2015. 3-3. DOI: 10.1109/RELENG.2015.10.
Garg, S. et al. On continuous integration/continuous delivery for automated deployment of machine learning models using MLOps. In: 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). IEEE. 2021. 25-28. DOI: 10.1109/AIKE52691.2021.00010.
Mei, S. et al. Model Provenance Management in MLOps Pipeline. In: 2022 The 8th International Conference on Computing and Data Engineering. 2022. 45-50. DOI: https://doi.org/10.1145/3512850.3512861.
Liu, Y. et al. Building A Platform for Machine Learning Operations from OpenSource Frameworks. IFAC-PapersOnLine. 2020; 53(5): 704-709. DOI: https://doi.org/10.1016/j.ifacol.2021.04.161.
Raj, E. Engineering MLOps. Packt Publishing, 2021.
Pahl, M., Loipfinger, M. Machine Learning as a reusable microservice. In: NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium. IEEE. 2018. 1-7. DOI: 10.1109/NOMS.2018.8406165.
Duvvuri, V. Minerva: A portable Machine Learning Microservice Framework for Traditional Enterprise SaaS applications. arXiv preprint arXiv:2005.00866, 2020.
Ribeiro, J. L. et al. A microservice based architecture topology for machine learning deployment. In: 2019 IEEE International Smart Cities Conference (ISC2). IEEE. 2019. 426-431. DOI: 10.1109/ISC246665.2019.9071708.
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