In this contribution, we give an insight in our experiences in the technical and organizational realization of industrial analytics. We address challenges in implementing industrial analytics in real-world applications and discuss aspects to consider when designing a machine learning solution for production. We focus on technical and organizational aspects to make industrial analytics work for real-world applications in factory automation. As an example, we consider a machine learning use case in the area of industry compressors. We discuss the importance of scalability and reusability of data analytics pipelines and present a container-based system architecture.

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http://dx.doi.org/10.1007/978-3-662-58485-9_13 under the license cc-by
https://academic.microsoft.com/#/detail/2905317108 under the license http://creativecommons.org/licenses/by/4.0
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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.1007/978-3-662-58485-9_13
Licence: CC BY-NC-SA license

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