Software Engineering Patterns for Machine Learning Applications (SEP4MLA)

Software Engineering Patterns for Machine Learning Applications (SEP4MLA). Washizaki, H., Khomh, F., & Gu�h�neuc, Y. In Cheng, Y., Iba, T., & Ni, W., editors, Proceedings of the 9th Asian Conference on Pattern Languages of Programs (AsianPLoP), September, 2020. ACM Press. 10 pages.

Paper abstract bibtex

To grasp the landscape of software engineering patterns for machine learning (ML) applications, a systematic literature review of both academic and gray literature is conducted to collect good and bad software-engineering practices in the form of patterns and anti-patterns for ML applications. From the 32 scholarly documents and 48 gray documents identified, we extracted 12 ML architecture patterns, 13 ML design patterns, and 8 ML anti-patterns. From these 33 ML patterns, we describe three major ML architecture patterns (``Data Lake'', ``Distinguish Business Logic from ML Models'', and ``Microservice Architecture'') and one ML design pattern (``ML Versioning'') in the standard pattern format so that practitioners can (re)use them in their contexts.

@INPROCEEDINGS{Washizaki20-AsianPLoP-SEP4MLAPart1,
   AUTHOR       = {Hironori Washizaki and Foutse Khomh and 
      Yann-Ga�l Gu�h�neuc},
   BOOKTITLE    = {Proceedings of the 9<sup>th</sup> Asian Conference on Pattern Languages of Programs (AsianPLoP)},
   TITLE        = {Software Engineering Patterns for Machine Learning 
      Applications (SEP4MLA)},
   YEAR         = {2020},
   OPTADDRESS   = {},
   OPTCROSSREF  = {},
   EDITOR       = {Yung-Pin Cheng and Takashi Iba and Wen-Chun Ni},
   MONTH        = {September},
   NOTE         = {10 pages.},
   OPTNUMBER    = {},
   OPTORGANIZATION = {},
   OPTPAGES     = {},
   PUBLISHER    = {ACM Press},
   OPTSERIES    = {},
   OPTVOLUME    = {},
   KEYWORDS     = {Topic: <b>Design patterns</b>, Venue: <c>PLoP</c>},
   URL          = {http://www.ptidej.net/publications/documents/AsianPLoP20.doc.pdf},
   ABSTRACT     = {To grasp the landscape of software engineering patterns 
      for machine learning (ML) applications, a systematic literature 
      review of both academic and gray literature is conducted to collect 
      good and bad software-engineering practices in the form of patterns 
      and anti-patterns for ML applications. From the 32 scholarly 
      documents and 48 gray documents identified, we extracted 12 ML 
      architecture patterns, 13 ML design patterns, and 8 ML anti-patterns. 
      From these 33 ML patterns, we describe three major ML architecture 
      patterns (``Data Lake'', ``Distinguish Business Logic from ML 
      Models'', and ``Microservice Architecture'') and one ML design 
      pattern (``ML Versioning'') in the standard pattern format so that 
      practitioners can (re)use them in their contexts.}
}

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