A Framework for the Systematic Review of ML Assets in AI Registries
Background: Modern software systems increasingly rely on Machine Learning (ML) assets (i.e., pre-trained models, datasets, benchmarks) for building, evaluating, and integrating ML-based systems.
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Key points
- However, current exploration, selection and reuse practices of ML assets are not supported by systematic retrieval methodologies comparable to those used in traditional evidence synthesis.
- Aims: This paper explores how systematic review methods can support ML asset retrieval.
- The resulting framework integrates registry-aware search strategies, cross-registry schema alignment, and dependency-driven ML asset exploration.
- Conclusions: This work illustrates how systematic review principles can be extended beyond scientific literature to support evidence synthesis over evolving AI registries.
Sources (1)
- [1]A Framework for the Systematic Review of ML Assets in AI RegistriesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 06:50 AM
Background: Modern software systems increasingly rely on Machine Learning (ML) assets (i.e., pre-trained models, datasets, benchmarks) for building, evaluating, and integrating ML-based systems.
However, current exploration, selection and reuse practices of ML assets are not supported by systematic retrieval methodologies comparable to those used in traditional evidence synthesis.
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