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A Model Management Platform for Industry 4.0 – Enabling Management of Machine Learning Models in Manufacturing Environments

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Business Information Systems (BIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 389))

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Abstract

Industry 4.0 use cases such as predictive maintenance and product quality control make it necessary to create, use and maintain a multitude of different machine learning models. In this setting, model management systems help to organize models. However, concepts for model management systems currently focus on data scientists, but do not support non-expert users such as domain experts and business analysts. Thus, it is difficult for them to reuse existing models for their use cases. In this paper, we address these challenges and present an architecture, a metadata schema and a corresponding model management platform.

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Notes

  1. 1.

    Pickle: https://docs.python.org/3/library/pickle.html.

  2. 2.

    RDS: https://www.rdocumentation.org/packages/base/versions/3.6.1/topics/readRDS.

  3. 3.

    HDF5: https://www.hdfgroup.org/solutions/hdf5/.

  4. 4.

    jpmml: https://github.com/jpmml.

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Author information

Authors and Affiliations

  1. Graduate School of Advanced Manufacturing Engineering, University of Stuttgart, Stuttgart, Germany

    Christian Weber & Peter Reimann

  2. Institute for Parallel and Distributed Systems, University of Stuttgart, Stuttgart, Germany

    Pascal Hirmer & Peter Reimann

Authors
  1. Christian Weber
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  2. Pascal Hirmer
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  3. Peter Reimann
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Corresponding author

Correspondence to Christian Weber .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznan, Poland

    Witold Abramowicz

  2. University of Colorado, Colorado Springs, CO, USA

    Gary Klein

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Weber, C., Hirmer, P., Reimann, P. (2020). A Model Management Platform for Industry 4.0 – Enabling Management of Machine Learning Models in Manufacturing Environments. In: Abramowicz, W., Klein, G. (eds) Business Information Systems. BIS 2020. Lecture Notes in Business Information Processing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-53337-3_30

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  • DOI: https://doi.org/10.1007/978-3-030-53337-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-53336-6

  • Online ISBN: 978-3-030-53337-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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