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The different versions of the original document can be found in: | The different versions of the original document can be found in: | ||
− | * [https://link.springer.com/content/pdf/10.1007%2F978-3-662-58485-9_13.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-662-58485-9_13.pdf] under the license | + | * [https://link.springer.com/content/pdf/10.1007%2F978-3-662-58485-9_13.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-662-58485-9_13.pdf] under the license https://creativecommons.org/licenses/by |
− | * [http://link.springer.com/content/pdf/10.1007/978-3-662-58485-9_13 http://link.springer.com/content/pdf/10.1007/978-3-662-58485-9_13],[http://dx.doi.org/10.1007/978-3-662-58485-9_13 http://dx.doi.org/10.1007/978-3-662-58485-9_13] under the license | + | * [http://link.springer.com/content/pdf/10.1007/978-3-662-58485-9_13 http://link.springer.com/content/pdf/10.1007/978-3-662-58485-9_13], |
+ | : [http://dx.doi.org/10.1007/978-3-662-58485-9_13 http://dx.doi.org/10.1007/978-3-662-58485-9_13] under the license cc-by | ||
− | * [https://link.springer.com/chapter/10.1007/978-3-662-58485-9_13 https://link.springer.com/chapter/10.1007/978-3-662-58485-9_13],[https://academic.microsoft.com/#/detail/2905317108 https://academic.microsoft.com/#/detail/2905317108] | + | * [https://link.springer.com/chapter/10.1007/978-3-662-58485-9_13 https://link.springer.com/chapter/10.1007/978-3-662-58485-9_13], |
+ | : [https://www.scipedia.com/public/Koester_2018a https://www.scipedia.com/public/Koester_2018a], | ||
+ | : [https://dblp.uni-trier.de/db/conf/ml4cps/ml4cps2018.html#Koester18 https://dblp.uni-trier.de/db/conf/ml4cps/ml4cps2018.html#Koester18], | ||
+ | : [https://academic.microsoft.com/#/detail/2905317108 https://academic.microsoft.com/#/detail/2905317108] under the license http://creativecommons.org/licenses/by/4.0 |
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.
Document type: Part of book or chapter of book
The different versions of the original document can be found in:
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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