| Item type |
Symposium(1) |
| 公開日 |
2024-09-10 |
| タイトル |
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|
タイトル |
An MLOps Workflow for Automotive System Development |
| タイトル |
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言語 |
en |
|
タイトル |
An MLOps Workflow for Automotive System Development |
| 言語 |
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|
言語 |
eng |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
ポスター論文 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
|
資源タイプ |
conference paper |
| 著者所属 |
|
|
|
Hitachi Ltd. |
| 著者所属 |
|
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|
Hitachi Ltd. |
| 著者所属 |
|
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|
Hitachi Astemo Ltd. |
| 著者所属 |
|
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Hitachi Astemo Ltd. |
| 著者所属 |
|
|
|
Hitachi Astemo Ltd. |
| 著者所属 |
|
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|
Hitachi Ltd. |
| 著者所属(英) |
|
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|
en |
|
|
Hitachi Ltd. |
| 著者所属(英) |
|
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|
en |
|
|
Hitachi Ltd. |
| 著者所属(英) |
|
|
|
en |
|
|
Hitachi Astemo Ltd. |
| 著者所属(英) |
|
|
|
en |
|
|
Hitachi Astemo Ltd. |
| 著者所属(英) |
|
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|
en |
|
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Hitachi Astemo Ltd. |
| 著者所属(英) |
|
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|
en |
|
|
Hitachi Ltd. |
| 著者名 |
Siqiao, Li
Makoto, Ichii
Masahiro, Matsubara
Kei, Kawahara
Hiroki, Maehama
Yasufumi, Suzuki
|
| 著者名(英) |
Siqiao, Li
Makoto, Ichii
Masahiro, Matsubara
Kei, Kawahara
Hiroki, Maehama
Yasufumi, Suzuki
|
| 論文抄録 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Automotive software engineering is increasingly being combined with machine learning (ML) technologies, which makes the development of high-quality automotive software more challenging. Automotive SPICE is an assessment model, and the latest version 4.0 emphasizes the specific requirements of ML engineering associated with automotive software, aiming to ensure the high-quality of development process. The development of the ML model for the automotive also tends to use experimental methods, so efficiency is also a topic that needs attention. This paper discusses the applicability of Machine Learning Operations (MLOps) in Automotive SPICE 4.0 and designs it for automotive and related ML development. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Automotive software engineering is increasingly being combined with machine learning (ML) technologies, which makes the development of high-quality automotive software more challenging. Automotive SPICE is an assessment model, and the latest version 4.0 emphasizes the specific requirements of ML engineering associated with automotive software, aiming to ensure the high-quality of development process. The development of the ML model for the automotive also tends to use experimental methods, so efficiency is also a topic that needs attention. This paper discusses the applicability of Machine Learning Operations (MLOps) in Automotive SPICE 4.0 and designs it for automotive and related ML development. |
| 書誌情報 |
ソフトウェアエンジニアリングシンポジウム2024論文集
巻 2024,
p. 313-314,
発行日 2024-09-10
|
| 出版者 |
|
|
言語 |
ja |
|
出版者 |
情報処理学会 |
IPSJ-SES2024050.pdf (479.9 kB)
