GenAIOps for GenAI Model-Agility
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866915171115466752 |
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| author | Ueno, Ken Kogo, Makoto Kawatsu, Hiromi Uchiumi, Yohsuke Tatsubori, Michiaki |
| author_facet | Ueno, Ken Kogo, Makoto Kawatsu, Hiromi Uchiumi, Yohsuke Tatsubori, Michiaki |
| contents | AI-agility, with which an organization can be quickly adapted to its business priorities, is desired even for the development and operations of generative AI (GenAI) applications. Especially in this paper, we discuss so-called GenAI Model-agility, which we define as the readiness to be flexibly adapted to base foundation models as diverse as the model providers and versions. First, for handling issues specific to generative AI, we first define a methodology of GenAI application development and operations, as GenAIOps, to identify the problem of application quality degradation caused by changes to the underlying foundation models. We study prompt tuning technologies, which look promising to address this problem, and discuss their effectiveness and limitations through case studies using existing tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17440 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GenAIOps for GenAI Model-Agility Ueno, Ken Kogo, Makoto Kawatsu, Hiromi Uchiumi, Yohsuke Tatsubori, Michiaki Software Engineering Machine Learning 68-04 I.2.0; D.2.0 AI-agility, with which an organization can be quickly adapted to its business priorities, is desired even for the development and operations of generative AI (GenAI) applications. Especially in this paper, we discuss so-called GenAI Model-agility, which we define as the readiness to be flexibly adapted to base foundation models as diverse as the model providers and versions. First, for handling issues specific to generative AI, we first define a methodology of GenAI application development and operations, as GenAIOps, to identify the problem of application quality degradation caused by changes to the underlying foundation models. We study prompt tuning technologies, which look promising to address this problem, and discuss their effectiveness and limitations through case studies using existing tools. |
| title | GenAIOps for GenAI Model-Agility |
| topic | Software Engineering Machine Learning 68-04 I.2.0; D.2.0 |
| url | https://arxiv.org/abs/2502.17440 |