# ML-Asset Management: Curation, Discovery, and Utilization

arXiv: https://arxiv.org/abs/2509.23577 · HTML: https://mlsanity.com/paper/2509.23577/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 80 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 23 of 23)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024)
- [Data Selection for Language Models via Importance Resampling](https://mlsanity.com/paper/2302.03169/) (2023)
- [LEEP: A New Measure to Evaluate Transferability of Learned Representations](https://mlsanity.com/paper/2002.12462/) (2020)
- [LogME: Practical Assessment of Pre-trained Models for Transfer Learning](https://mlsanity.com/paper/2102.11005/) (2021)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Similarity of Neural Network Models: A Survey of Functional and Representational Measures](https://mlsanity.com/paper/2305.06329/) (2023)
- [Croissant: A Metadata Format for ML-Ready Datasets](https://mlsanity.com/paper/2403.19546/) (2024)
- [Scientific Machine Learning Benchmarks](https://mlsanity.com/paper/2110.12773/) (2021)
- [Behavioral Use Licensing for Responsible AI](https://mlsanity.com/paper/2011.03116/) (2020)
- [Survey of Vector Database Management Systems](https://mlsanity.com/paper/2310.14021/) (2023)
- [Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning](https://mlsanity.com/paper/2210.01922/) (2022)
- [Dataset search: a survey](https://mlsanity.com/paper/1901.00735/) (2019)
- [ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data](https://mlsanity.com/paper/2403.04871/) (2024)
- [KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics](https://mlsanity.com/paper/1610.09451/) (2016)
- [Language Models as a Service: Overview of a New Paradigm and its Challenges](https://mlsanity.com/paper/2309.16573/) (2023)
- [Benchmark Data Repositories for Better Benchmarking](https://mlsanity.com/paper/2410.24100/) (2024)
- [View-based Explanations for Graph Neural Networks](https://mlsanity.com/paper/2401.02086/) (2024)
- [Data Market Platforms: Trading Data Assets to Solve Data Problems](https://mlsanity.com/paper/2002.01047/) (2020)
- [Implementing AI Bill of Materials (AI BOM) with SPDX 3.0: A Comprehensive Guide to Creating AI and Dataset Bill of Materials](https://mlsanity.com/paper/2504.16743/) (2025)
- [Model Selection with Model Zoo via Graph Learning](https://mlsanity.com/paper/2404.03988/) (2024)
- ["They've Stolen My GPL-Licensed Model!": Toward Standardized and Transparent Model Licensing](https://mlsanity.com/paper/2412.11483/) (2024)
- [Generating Skyline Datasets for Data Science Models](https://mlsanity.com/paper/2502.11262/) (2025)

## Built on by (most cited first, 1 of 1)

- [Discovering Hidden Gems in Model Repositories](https://mlsanity.com/paper/2601.22157/) (2026)

## Abstract (arXiv)

Machine learning (ML) assets, such as models, datasets, and metadata, are central to modern ML workflows. Despite their explosive growth in practice, these assets are often underutilized due to fragmented documentation, siloed storage, inconsistent licensing, and lack of unified discovery mechanisms, making ML-asset management an urgent challenge. This tutorial offers a comprehensive overview of ML-asset management activities across its lifecycle, including curation, discovery, and utilization. We provide a categorization of ML assets, and major management issues, survey state-of-the-art techniques, and identify emerging opportunities at each stage. We further highlight system-level challenges related to scalability, lineage, and unified indexing. Through live demonstrations of systems, this tutorial equips both researchers and practitioners with actionable insights and practical tools for advancing ML-asset management in real-world and domain-specific settings.