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ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems

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Software Quality as a Foundation for Security (SWQD 2024)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 505))

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Abstract

[Context] Systems that incorporate Machine Learning (ML) models, often referred to as ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are engineered in practice is still limited; this is especially true for activities surrounding ML model dissemination. [Goal] We investigate contemporary industrial practices and problems related to ML model dissemination, focusing on the model deployment and the monitoring ML life cycle phases. [Method] We conducted an international survey to gather practitioner insights on how ML-enabled systems are engineered. We gathered a total of 188 complete responses from 25 countries. We analyze the status quo and problems reported for the model deployment and monitoring phases. We analyzed contemporary practices using bootstrapping with confidence intervals and conducted qualitative analyses on the reported problems applying open and axial coding procedures. [Results] Practitioners perceive the model deployment and monitoring phases as relevant and difficult. With respect to model deployment, models are typically deployed as separate services, with limited adoption of MLOps principles. Reported problems include difficulties in designing the architecture of the infrastructure for production deployment and legacy application integration. Concerning model monitoring, many models in production are not monitored. The main monitored aspects are inputs, outputs, and decisions. Reported problems involve the absence of monitoring practices, the need to create custom monitoring tools, and the selection of suitable metrics. [Conclusion] Our results help provide a better understanding of the adopted practices and problems in practice and support guiding ML deployment and monitoring research in a problem-driven manner.

G. Giray—Independent Researcher.

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Notes

  1. 1.

    https://doi.org/10.5281/zenodo.10092394.

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Author information

Authors and Affiliations

  1. Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil

    Eduardo Zimelewicz, Marcos Kalinowski, Antonio Pedro Santos Alves, Kelly Azevedo, Hugo Villamizar, Tatiana Escovedo & Helio Lopes

  2. Blekinge Institute of Technology (BTH), Karlskrona, Sweden

    Daniel Mendez, Niklas Lavesson & Tony Gorschek

  3. Izmir, Turkey

    Görkem Giray

  4. Vienna University of Technology (TU Wien), Vienna, Austria

    Stefan Biffl & Juergen Musil

  5. German Aerospace Center (DLR), Cologne, Germany

    Michael Felderer

  6. University of Cologne, Cologne, Germany

    Michael Felderer

  7. Technical University of Munich, Munich, Germany

    Stefan Wagner

  8. University of Bari, Bari, Italy

    Teresa Baldassarre

  9. fortiss GmbH, Munich, Germany

    Daniel Mendez & Tony Gorschek

Authors
  1. Eduardo Zimelewicz
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  2. Marcos Kalinowski
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  3. Daniel Mendez
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  7. Kelly Azevedo
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  8. Hugo Villamizar
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  9. Tatiana Escovedo
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  13. Michael Felderer
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  14. Stefan Wagner
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  15. Teresa Baldassarre
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Corresponding author

Correspondence to Marcos Kalinowski .

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Editors and Affiliations

  1. fortiss GmbH, Munich, Germany

    Peter Bludau

  2. Software Competence Center Hagenberg GmbH, Hagenberg, Austria

    Rudolf Ramler

  3. Austrian Center for Digital Production (CDP), SBA Research gGmbH, Vienna, Austria

    Dietmar Winkler

  4. Software Quality Lab GmbH, Linz, Austria

    Johannes Bergsmann

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Zimelewicz, E. et al. (2024). ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems. In: Bludau, P., Ramler, R., Winkler, D., Bergsmann, J. (eds) Software Quality as a Foundation for Security. SWQD 2024. Lecture Notes in Business Information Processing, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-56281-5_7

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  • DOI: https://doi.org/10.1007/978-3-031-56281-5_7

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