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2023 | OriginalPaper | Chapter

8. Concept for Deployment Design of Machine Learning Models in Production

Authors : Henrik Heymann, Andrés Boza

Published in: Industry 4.0: The Power of Data

Publisher: Springer International Publishing

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Abstract

The application of artificial intelligence (AI) and machine learning (ML) in production environments offers huge potential for the manufacturing industry. In order to create added value, ML models must be deployed into production which means making models available in a specific environment where the results are needed. As an initial task in deployment, called the deployment design, decision owners need to define the desired ML system architecture. The goal of this paper is to provide a structured methodology in form of a morphological box containing the available options for the deployment design. Through the review of gray literature, the five most relevant parameters are identified as prediction approach, consuming application, model serving, learning method, and hosting solution. Possible values for each parameter are introduced and necessary considerations for the selection of an option are discussed. By means of a case study in the context of predictive quality, which describes the use of a ML model to predict the product quality based on production data, the developed concept is applied and validated.

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Metadata
Title
Concept for Deployment Design of Machine Learning Models in Production
Authors
Henrik Heymann
Andrés Boza
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-29382-5_8

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