Assessment Framework for Deployability of Machine Learning Models in Production

Procedia CIRP, vol. 118, pp. 32–37

Abstract

Deploying machine learning (ML) models in production environments comes with challenges such as the model's 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.

Authors 4

1. Henrik Heymann corresponding Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

2. Hendrik Mende Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

3. Maik Frye Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

4. Robert Heinrich Schmitt Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany