Understanding and Supporting the ML Supply Chain Through ML Bill of Materials
Within the last decade, the Machine Learning (ML) supply chain has emerged with increasing complexity. This dissertation focuses on identifying and resolving the challenges faced by various stakeholders in the ML supply chain, including those relating to provenance and compliance tasks. These challe...
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| Veröffentlicht in: | Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) S. 1 - 3 |
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| Format: | Tagungsbericht |
| Sprache: | Englisch |
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IEEE
27.04.2025
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| ISSN: | 2574-1934 |
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| Abstract | Within the last decade, the Machine Learning (ML) supply chain has emerged with increasing complexity. This dissertation focuses on identifying and resolving the challenges faced by various stakeholders in the ML supply chain, including those relating to provenance and compliance tasks. These challenges will be identified through a combination of surveys, interviews, mining studies, and literature reviews. They will be addressed by employing Machine Learning Bills of Material (MLBOM) accompanied with appropriate automated tooling solutions. Our anticipated contributions include developing a rich understanding of practitioner needs, undertaking a comprehensive evaluation of the current ML supply chain, and implementing novel tooling solutions to assist ML supply chain stakeholders. |
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| AbstractList | Within the last decade, the Machine Learning (ML) supply chain has emerged with increasing complexity. This dissertation focuses on identifying and resolving the challenges faced by various stakeholders in the ML supply chain, including those relating to provenance and compliance tasks. These challenges will be identified through a combination of surveys, interviews, mining studies, and literature reviews. They will be addressed by employing Machine Learning Bills of Material (MLBOM) accompanied with appropriate automated tooling solutions. Our anticipated contributions include developing a rich understanding of practitioner needs, undertaking a comprehensive evaluation of the current ML supply chain, and implementing novel tooling solutions to assist ML supply chain stakeholders. |
| Author | Stalnaker, Trevor |
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| Snippet | Within the last decade, the Machine Learning (ML) supply chain has emerged with increasing complexity. This dissertation focuses on identifying and resolving... |
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| SubjectTerms | Bills of materials Complexity theory Interviews Machine learning Software engineering Stakeholders Supply chains Surveys Systematic literature review |
| Title | Understanding and Supporting the ML Supply Chain Through ML Bill of Materials |
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