ML-Asset Management: Curation, Discovery, and Utilization
Summary: Tutorial framing ML-asset management across the lifecycle—curation, discovery, utilization—cataloging models, datasets, and metadata while exposing fragmentation, siloed storage, licensing inconsistencies, and missing unified discovery. Surveys techniques, highlights system challenges (scalability, lineage, unified indexing), and demos practical systems to drive research and engineering toward searchable, reusable ML-asset infrastructure. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Mengying Wang
- 2. Moming Duan
- 3. Yicong Huang
- 4. Chen Li
- 5. Bingsheng He
- 6. Yinghui Wu
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