@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00239278,
 author = {Siqiao, Li and Makoto, Ichii and Masahiro, Matsubara and Kei, Kawahara and Hiroki, Maehama and Yasufumi, Suzuki and Siqiao, Li and Makoto, Ichii and Masahiro, Matsubara and Kei, Kawahara and Hiroki, Maehama and Yasufumi, Suzuki},
 book = {ソフトウェアエンジニアリングシンポジウム2024論文集},
 month = {Sep},
 note = {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., 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.},
 pages = {313--314},
 publisher = {情報処理学会},
 title = {An MLOps Workflow for Automotive System Development},
 volume = {2024},
 year = {2024}
}