Abstract
Machine learning (ML) techniques have been introduced into various domains in recent years. Thus, it is important to construct reusable knowledge on projects that develop ML-based service systems to implement such projects effectively. In this study, the collection of insights and as well as the development of architecture and design patterns for ML-based service systems are considered. We propose a method for collecting insights and developing patterns for ML projects by referring a development model based on project practices.
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References
S. Amershi, A. Begel, C. Bird, R. Deliner, H. Gall, E. Kamar, N.N.B. Nushi, T. Zimmermann, Software engineering for machine learning: a case study, in Proceedings of the 41st International Conference on Software Engineering (2019), pp. 291–300
Y. Demchenko, C. de Last, P. Membrey, Defining architecture components of the big data ecosystem, in Proceedings of the International Conference on Collaboration Technologies and Systems (CTS) (2014), pp. 104–112
S. Earley, Analytics, machine learning, and the internet of things. IEEE ITPro 17(1), 10–13 (2015)
J. Heit, J. Liu, M. Shah, An architecture for the deployment of statistical models for the big data era, in Proceedings of IEEE International Conference on Big Data (2016), pp. 1377–1384
M. Kim, T. Zimmermann, R. DeLine, A. Begel, The emerging role of data scientists on software development teams, in Proceedings of the 38th International Conference on Software Engineering (2016), pp. 96–107
F. Kumeno, Software engineering challenges for machine learning applications: a literature review. Intell. Decis. Technol. 13, 463–476 (2019)
V. Lakshmanan, S. Robinson, M. Mann, Machine learning design patterns: solutions to common challenges in data preparation, Model Building, and MLOps. O’Reilly (2020)
L.E. Lwakatare, A. Raj, J. Bosch, H.H. Olsson, I. Crnkovic, A taxonomy of software engineering challenges for machine learning systems: an empirical investigation, in Proceedings of the 20th International Conference on Agile Software Development (XP) (2019), pp. 227–243
A. Serban, K. van der Blom, H. Hoos, J. Visser, Adoption and effects of software engineering best practices in machine learning, in Proceedings of the ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (2020), pp. 3:1–3:12
H. Takeuchi, T. Doi, H. Washizaki, S. Okuda, N. Yoshioka, Enterprise architecture based representation of architecture and design patterns for machine learning systems, in Proceedings of the 13th Workshop on Service oriented Enterprise Architecture for Enterprise Engineering (IEEE 25th EDOC Workshop) (2021), pp. 246–250
H. Takeuchi, H. Kaiya, H. Nakagawa, S. Ogata, Reference model for agile development of machine learning-based service systems, in Proceedings of the 3rd International Workshop on Machine Learning Systems Engineering (Companion Proceedings of the 28th Asia-Pacific Software Engineering Conference) (2021), pp. 115–118
H. Takeuchi, S. Yamamoto, Ai service system development using enterprise architecture modeling, in Proceedings of the 23rd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (Procedia Computer Science vol. 159) (2019), pp. 923–932
The Open Group: ArchiMate 3.1—A Pocket Guide. Van Hares Publishing (2019)
H. Washizaki, F. Khomh, Y.G. Guéhéneuc, H. Takeuchi, S. Okuda, N. Natori, N. Shioura, Software engineering patterns for machine learning applications (SEP4MLA)—part 2, in Proceedings of the 27th Conference on Pattern Languages of Programs (PLoP 2020) (2020)
H. Washizaki, H. Uchida, F. Khomh, Y.G. Guéhéneuc, Software engineering patterns for machine learning applications (SEP4MLA), in Proceedings of the 9th Asian Conference on Pattern Languages of Programs (AsianPLoP 2020) (2020)
H. Yokoyama, Machine learning system architectural pattern for improving operational stability, in Proceedings of IEEE International Conference on Software Architecture Companion (2019), pp. 267–274
A. Zimmermann, R. Schmidt, D. Jugel, M. Möhring, Evolution of enterprise architecture for intelligent digital systems, in Proceedings of the 14th International Conference on Research Challenges on Information Science (2020), pp. 145–153
A. Zimmermann, R. Schmidt, K. Sandkuhl, D. Jugel, J. Bogner, M. Möhring, Evolution of enterprise architecture for digital transformation, in Proceedings of the IEEE 22nd International Enterprise Distributed Object Computing Workshop (2018), pp. 87–96
Acknowledgements
This work was supported by a JSPS Grant-in-Aid for Scientific Research (KAKENHI), Grant No. JP19K20416, and the JST-Mirai Project (Engineerable AI Techniques for Practical Applications of High-Quality Machine Learning-based Systems), Grant No. JPMJMI20B8.
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Takeuchi, H., Imazaki, K., Kuno, N., Doi, T., Motohashi, Y. (2023). Collecting Insights and Developing Patterns for Machine Learning Projects Based on Project Practices. In: Virvou, M., Saruwatari, T., Jain, L.C. (eds) Knowledge-Based Software Engineering: 2022. JCKBSE 2022. Learning and Analytics in Intelligent Systems, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-031-17583-1_5
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DOI: https://doi.org/10.1007/978-3-031-17583-1_5
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