Systematic Mapping Study on the Machine Learning Lifecycle

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

External Research Organisations

  • ING Groep N.V.
  • Fontys Venlo University of Applied Sciences
  • Delft University of Technology
View graph of relations

Details

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, WAIN 2021
Pages70-73
Number of pages4
ISBN (electronic)9781665444705
Publication statusPublished - 2021
Externally publishedYes
EventWAIN'21 - 1st Workshop on AI Engineering – Software Engineering for AI - Virtual, Spain
Duration: 30 May 202131 May 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.

Keywords

    AI lifecycle management, Artificial Intelligence, Software Engineering, Systematic mapping study

ASJC Scopus subject areas

Cite this

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. p. 70-73 9474380.

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer 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, pp. 70-73, WAIN'21 - 1st Workshop on AI Engineering – Software Engineering for AI, Spain, 30 May 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 (pp. 70-73). Article 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. p. 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. pp. 70-73
Download
@inproceedings{a35d8850b7534b0a8b2246ab1a8b7f08,
title = "Systematic Mapping Study on the Machine Learning Lifecycle",
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.",
keywords = "AI lifecycle management, Artificial Intelligence, Software Engineering, Systematic mapping study",
author = "Yuanhao Xie and {Miranda da Cruz}, Luis and Petra Heck and Jan Rellermeyer",
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",
year = "2021",
doi = "10.1109/WAIN52551.2021.00017",
language = "English",
isbn = "9781665444712",
pages = "70--73",
booktitle = "Proceedings - 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, WAIN 2021",

}

Download

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 -

By the same author(s)