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Authors = Florian Sobiezky ORCID = 0000-0001-5228-0153

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28 pages, 3799 KiB  
Review
AI System Engineering—Key Challenges and Lessons Learned
by Lukas Fischer, Lisa Ehrlinger, Verena Geist, Rudolf Ramler, Florian Sobiezky, Werner Zellinger, David Brunner, Mohit Kumar and Bernhard Moser
Mach. Learn. Knowl. Extr. 2021, 3(1), 56-83; https://doi.org/10.3390/make3010004 - 31 Dec 2020
Cited by 41 | Viewed by 22563
Abstract
The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models, data and software quality issues [...] Read more.
The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models, data and software quality issues and human-centered artificial intelligence (AI) postulates, including confidentiality and ethical aspects. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment. The aim of this paper is to pinpoint research topics to explore approaches to address these challenges. Full article
(This article belongs to the Special Issue Selected Papers from CD-MAKE 2020 and ARES 2020)
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