{"id":3092,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3092"},"modified":"2022-07-26T13:06:54","modified_gmt":"2022-07-26T11:06:54","slug":"assessment-framework-for-deployability-of-machine-learning-models-in-production","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/production-systems-networks-2\/assessment-framework-for-deployability-of-machine-learning-models-in-production\/","title":{"rendered":"Assessment framework for deployability of machine learning models in production"},"content":{"rendered":"\n

by Henrik Heymann, Hendrik Mende, Maik Frye, Robert H. Schmitt (Germany)<\/em><\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

Deploying machine learning (ML) models in production environments comes with challenges such as the model\u00d5s integration into live production and the missing trust of process experts in new technologies. These challenges must be addressed already in phases ahead of the deployment. Therefore, this paper aims to clarify how to ensure the deployability of methods used during model development. For this purpose, criteria for measuring and evaluating deployability in manufacturing environments are defined. A subsequent analysis of existing data preprocessing methods and ML algorithms regarding deployability as well as deployment options serves to counteract deployment issues early on in an ML project.<\/p>\n\n\n\n

Keywords<\/strong>: Artifical Intelligence; Machine Learning; Deployment; Deployability; Production; Manufacturing<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n