@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00212679,
 author = {Valliappa, Lakshmanan and Valliappa, Lakshmanan},
 book = {ソフトウェアエンジニアリングシンポジウム2021論文集},
 month = {Aug},
 note = {Design patterns are formalized best practices to solve common problems when designing a software system. As machine learning moves from being a research discipline to a software one, it is useful to catalog tried-and-proven methods to help engineers tackle frequently occurring problems that crop up during the ML process. In this talk, I will cover five patterns (Hashed Feature, Neutral Class, Stateless Serving Function, Bridged Schema, Feature Store) that academic researchers often don’t think about but are very useful in practical situations that arise in industry. For data scientists and ML engineers, these patterns provide a way to apply hard-won knowledge from hundreds of ML experts to your own projects., Design patterns are formalized best practices to solve common problems when designing a software system. As machine learning moves from being a research discipline to a software one, it is useful to catalog tried-and-proven methods to help engineers tackle frequently occurring problems that crop up during the ML process. In this talk, I will cover five patterns (Hashed Feature, Neutral Class, Stateless Serving Function, Bridged Schema, Feature Store) that academic researchers often don’t think about but are very useful in practical situations that arise in industry. For data scientists and ML engineers, these patterns provide a way to apply hard-won knowledge from hundreds of ML experts to your own projects.},
 pages = {12--12},
 publisher = {情報処理学会},
 title = {Machine Learning Design Patterns in Industry},
 volume = {2021},
 year = {2021}
}