Details
Originalsprache | Englisch |
---|---|
Titel des Sammelwerks | Proceedings - 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, WAIN 2021 |
Seiten | 70-73 |
Seitenumfang | 4 |
ISBN (elektronisch) | 9781665444705 |
Publikationsstatus | Veröffentlicht - 2021 |
Extern publiziert | Ja |
Veranstaltung | WAIN'21 - 1st Workshop on AI Engineering – Software Engineering for AI - Virtual, Spanien Dauer: 30 Mai 2021 → 31 Mai 2021 |
Abstract
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Artificial intelligence
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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/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Systematic Mapping Study on the Machine Learning Lifecycle
AU - Xie, Yuanhao
AU - Miranda da Cruz, Luis
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.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - AI lifecycle management
KW - Artificial Intelligence
KW - Software Engineering
KW - Systematic mapping study
UR - http://www.scopus.com/inward/record.url?scp=85113221203&partnerID=8YFLogxK
U2 - 10.1109/WAIN52551.2021.00017
DO - 10.1109/WAIN52551.2021.00017
M3 - Conference contribution
SN - 9781665444712
SP - 70
EP - 73
BT - Proceedings - 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, WAIN 2021
T2 - WAIN'21 - 1st Workshop on AI Engineering – Software Engineering for AI
Y2 - 30 May 2021 through 31 May 2021
ER -