A Framework to Model ML Engineering Processes
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917760549781504 |
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| author | Morales, Sergio Clarisó, Robert Cabot, Jordi |
| author_facet | Morales, Sergio Clarisó, Robert Cabot, Jordi |
| contents | The development of Machine Learning (ML) based systems is complex and requires multidisciplinary teams with diverse skill sets. This may lead to communication issues or misapplication of best practices. Process models can alleviate these challenges by standardizing task orchestration, providing a common language to facilitate communication, and nurturing a collaborative environment. Unfortunately, current process modeling languages are not suitable for describing the development of such systems. In this paper, we introduce a framework for modeling ML-based software development processes, built around a domain-specific language and derived from an analysis of scientific and gray literature. A supporting toolkit is also available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18531 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Framework to Model ML Engineering Processes Morales, Sergio Clarisó, Robert Cabot, Jordi Software Engineering Artificial Intelligence Machine Learning The development of Machine Learning (ML) based systems is complex and requires multidisciplinary teams with diverse skill sets. This may lead to communication issues or misapplication of best practices. Process models can alleviate these challenges by standardizing task orchestration, providing a common language to facilitate communication, and nurturing a collaborative environment. Unfortunately, current process modeling languages are not suitable for describing the development of such systems. In this paper, we introduce a framework for modeling ML-based software development processes, built around a domain-specific language and derived from an analysis of scientific and gray literature. A supporting toolkit is also available. |
| title | A Framework to Model ML Engineering Processes |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2404.18531 |