Software Engineering Patterns for Machine Learning Applications (SEP4MLA) - Part 4 - ML Gateway Routing Architecture. Washizaki, H., Khomh, F., & Gu�h�neuc, Y. In Manns, M. L. & Guerra, E., editors, Proceedings of the 29<sup>th</sup> Conference on Pattern Languages of Programs (PLoP), pages 1–10, October, 2022. ACM Press. 10 pages.
Software Engineering Patterns for Machine Learning Applications (SEP4MLA) - Part 4 - ML Gateway Routing Architecture [pdf]Paper  abstract   bibtex   
Machine learning (ML) researchers study the best practices to develop and support ML-based applications to ensure quality and determine the constraints applied to their application pipelines. Such practices are often formalized as software patterns. We discovered software-engineering design patterns for machine-learning applications by thoroughly searching the available literature on the subject. Among the ML patterns found, we describe in this paper one ML topology pattern, ``ML Gateway Routing Architecture'', in the standard pattern format so that practitioners can (re)use it in their contexts and benefits. The pattern addresses the problem of tight coupling among ML-implemented and non-ML business logic as well as the front-end client by installing a gateway that routes requests.

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