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QUA³CK - A Machine Learning Development Process

Stock, Simon Claus; Becker, Jürgen; Grimm, Daniel; Hotfilter, Tim; Molinar, Gabriela; Stang, Marco; Stork, Wilhelm

Machine learning and data processing are trending topics at the moment. However, there is still alack of a standard process to support a fast, simple, and effective development of machine learningmodels for academia and industry combined. Processes such as KDD or CRISP-DM are highlyspecialized in data mining and business cases. Therefore, engineers often refer to individualapproaches to solve a machine learning problem. Especially in teaching, the lack of a standardprocess is a challenge. Students typically get a better understanding if a systematic approach tosolve problems is given to them. A challenge when formulating a machine learning developmentprocess is to provide standard actions that work on different use-cases. At the same time, it has tobe simple. Complex processes often lead to the wrong approach.The QUA³CK process was created at the Karlsruhe Institute of Technology to fill the gap inresearch and industry for a machine learning development process. However, the main focus wasto reach engineering students with an easy-to-remember, didactic way to solve machine learningproblems. This five-stage process starts with a machine learning question (Q), a problem thathas to be solved. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000129631
Veröffentlicht am 12.02.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technik der Informationsverarbeitung (ITIV)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 07.2020
Sprache Englisch
Identifikator ISSN: 1824-8039
KITopen-ID: 1000129631
Erschienen in Proceedings of Artificial Intelligence for Science, Industry and Society — PoS(AISIS2019)
Veranstaltung Symposium Artificial Intelligence for Science, Industry and Society (AISIS 2019), Mexiko-Stadt, Mexiko, 20.10.2019 – 25.12.2019
Verlag Scuola Internazionale Superiore di Studi Avanzati (SISSA)
Seiten 026
Serie Proceedings of Science ; 372
Nachgewiesen in Dimensions
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