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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Bibliographic Details
Published in:Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) pp. 1 - 3
Main Author: Stalnaker, Trevor
Format: Conference Proceeding
Language:English
Published: IEEE 27.04.2025
Subjects:
Bills of materials
Complexity theory
Interviews
Machine learning
Software engineering
Stakeholders
Supply chains
Surveys
Systematic literature review
ISSN:2574-1934
Online Access:Get full text
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Summary: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.
ISSN:2574-1934
DOI:10.1109/ICSE-Companion66252.2025.00044