Software Engineering Patterns for Machine Learning Applications (SEP4MLA) - Part 4

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 29th Conference on Pattern Languages of Programs (PLoP), pages 1–10, October, 2022. ACM Press. 10 pages.

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.

@INPROCEEDINGS{Washizaki22-PLoP-SEP4MLAPart4,
   AUTHOR       = {Hironori Washizaki and Foutse Khomh and 
      Yann-Ga�l Gu�h�neuc},
   BOOKTITLE    = {Proceedings of the 29<sup>th</sup> Conference on Pattern Languages of Programs (PLoP)},
   TITLE        = {Software Engineering Patterns for Machine Learning 
      Applications (SEP4MLA) - Part 4 - ML Gateway Routing Architecture},
   YEAR         = {2022},
   OPTADDRESS   = {},
   OPTCROSSREF  = {},
   EDITOR       = {Mary Lynn Manns and Eduardo Guerra},
   MONTH        = {October},
   NOTE         = {10 pages.},
   OPTNUMBER    = {},
   OPTORGANIZATION = {},
   PAGES        = {1&ndash;10},
   PUBLISHER    = {ACM Press},
   OPTSERIES    = {},
   OPTVOLUME    = {},
   KEYWORDS     = {Topic: <b>Design patterns</b>, 
      Rubrique : <b>patrons de conception</b>, Conference: PLoP},
   URL          = {http://www.ptidej.net/publications/documents/PLoP22.doc.pdf},
   ABSTRACT     = {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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