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 9<sup>th</sup> Asian Conference on Pattern Languages of Programs (AsianPLoP), September, 2020. ACM Press. 10 pages.
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Paper abstract bibtex
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>, Conference: PLoP},
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.}
}Downloads: 0
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