Software Engineering Patterns for Machine Learning Applications (SEP4MLA) - Part 2. Washizaki, H., Khomh, F., Gu�h�neuc, Y., Takeuchi, H., Okuda, S., Natori, N., & Shioura, N. In Proceedings of the 27<sup>th</sup> Conference on Pattern Languages Of Programs (PLoP), October, 2020. ACM Press. 10 pages.
Software Engineering Patterns for Machine Learning Applications (SEP4MLA) - Part 2 [pdf]Paper  abstract   bibtex   
Practitioners and researchers study best practices to develop and maintain ML application systems and software to address quality and constraint problems. Such practices are often formalized as software patterns. We discovered software-engineering design patterns for machine-learning applications by doing a thorough search of available literature on the subject. From these ML patterns, we describe three ML patterns (``Different Workloads in Different Computing Environments'', ``Encapsulate ML Models Within Rule-base Safeguards'', and ``Data Flows Up, Model Flows Down'') in the standard pattern format so that practitioners can (re)use them in their contexts.

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