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Increasing Robustness for Machine Learning Services in Challenging Environments: Limited Resources and No Label Feedback

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Intelligent Systems and Applications (IntelliSys 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 294))

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Abstract

The importance of deployed machine learning solutions has increased significantly in the past years due to the availability of data sources, computing capabilities and convenient tooling. However, technical challenges such as limited resources and computing power arise in many applications. We consider a scenario where a machine learning model is deployed in an environment where all computations need to be performed on a local computing unit. Furthermore, after deployment, the model does not receive any ground truth labels as feedback. We develop a two-step prediction method which combines an outlier detection with a robust machine learning model. This approach is evaluated based on a data set from a large German OEM. We can show that the prediction performance is increased significantly with our approach while fulfilling the restrictions in terms of memory and computational power. This way, we contribute to the practical applicability of machine learning models for real-world applications.

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Author information

Authors and Affiliations

  1. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Lucas Baier & Niklas Kühl

  2. Robert Bosch GmbH, Gerlingen, Germany

    Jörg Schmitt

Authors
  1. Lucas Baier
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  2. Niklas Kühl
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  3. Jörg Schmitt
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Corresponding author

Correspondence to Lucas Baier .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Baier, L., Kühl, N., Schmitt, J. (2022). Increasing Robustness for Machine Learning Services in Challenging Environments: Limited Resources and No Label Feedback. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-82193-7_57

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