Systematic Mapping Study on the Machine Learning Lifecycle

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autoren

Externe Organisationen

  • ING Groep N.V.
  • Fontys Venlo University of Applied Sciences
  • Delft University of Technology
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Details

OriginalspracheEnglisch
Titel des SammelwerksProceedings - 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, WAIN 2021
Seiten70-73
Seitenumfang4
ISBN (elektronisch)9781665444705
PublikationsstatusVeröffentlicht - 2021
Extern publiziertJa
VeranstaltungWAIN'21 - 1st Workshop on AI Engineering – Software Engineering for AI - Virtual, Spanien
Dauer: 30 Mai 202131 Mai 2021

Abstract

The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated. We address this gap by conducting a systematic mapping study on the lifecycle of AI model. Through quantitative research, we provide an overview of the field, identify research opportunities, and provide suggestions for future research. Our study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics. We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.

ASJC Scopus Sachgebiete

Zitieren

Systematic Mapping Study on the Machine Learning Lifecycle. / Xie, Yuanhao; Miranda da Cruz, Luis; Heck, Petra et al.
Proceedings - 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, WAIN 2021. 2021. S. 70-73 9474380.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Xie, Y, Miranda da Cruz, L, Heck, P & Rellermeyer, J 2021, Systematic Mapping Study on the Machine Learning Lifecycle. in Proceedings - 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, WAIN 2021., 9474380, S. 70-73, WAIN'21 - 1st Workshop on AI Engineering – Software Engineering for AI, Spanien, 30 Mai 2021. https://doi.org/10.1109/WAIN52551.2021.00017
Xie, Y., Miranda da Cruz, L., Heck, P., & Rellermeyer, J. (2021). Systematic Mapping Study on the Machine Learning Lifecycle. In Proceedings - 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, WAIN 2021 (S. 70-73). Artikel 9474380 https://doi.org/10.1109/WAIN52551.2021.00017
Xie Y, Miranda da Cruz L, Heck P, Rellermeyer J. Systematic Mapping Study on the Machine Learning Lifecycle. in Proceedings - 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, WAIN 2021. 2021. S. 70-73. 9474380 doi: 10.1109/WAIN52551.2021.00017
Xie, Yuanhao ; Miranda da Cruz, Luis ; Heck, Petra et al. / Systematic Mapping Study on the Machine Learning Lifecycle. Proceedings - 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, WAIN 2021. 2021. S. 70-73
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abstract = "The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated. We address this gap by conducting a systematic mapping study on the lifecycle of AI model. Through quantitative research, we provide an overview of the field, identify research opportunities, and provide suggestions for future research. Our study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics. We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.",
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note = "Funding Information: ACKNOWLEDGEMENT We would like to thank Jerry Brons and Elvan Kulan for their valuable feedback in this work. This study was supported by the ICAI lab AI for Fintech Research.; WAIN'21 - 1st Workshop on AI Engineering – Software Engineering for AI ; Conference date: 30-05-2021 Through 31-05-2021",
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AU - Heck, Petra

AU - Rellermeyer, Jan

N1 - Funding Information: ACKNOWLEDGEMENT We would like to thank Jerry Brons and Elvan Kulan for their valuable feedback in this work. This study was supported by the ICAI lab AI for Fintech Research.

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