Optimal Pricing for Data-Augmented AutoML Marketplaces
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arXiv
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| Format: | Preprint |
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2023
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| author | Han, Minbiao Light, Jonathan Xia, Steven Galhotra, Sainyam Fernandez, Raul Castro Xu, Haifeng |
| author_facet | Han, Minbiao Light, Jonathan Xia, Steven Galhotra, Sainyam Fernandez, Raul Castro Xu, Haifeng |
| contents | Organizations often lack sufficient data to effectively train machine learning (ML) models, while others possess valuable data that remains underutilized. Data markets promise to unlock substantial value by matching data suppliers with demand from ML consumers. However, market design involves addressing intricate challenges, including data pricing, fairness, robustness, and strategic behavior. In this paper, we propose a pragmatic data-augmented AutoML market that seamlessly integrates with existing cloud-based AutoML platforms such as Google's Vertex AI and Amazon's SageMaker. Unlike standard AutoML solutions, our design automatically augments buyer-submitted training data with valuable external datasets, pricing the resulting models based on their measurable performance improvements rather than computational costs as the status quo. Our key innovation is a pricing mechanism grounded in the instrumental value - the marginal model quality improvement - of externally sourced data. This approach bypasses direct dataset pricing complexities, mitigates strategic buyer behavior, and accommodates diverse buyer valuations through menu-based options. By integrating automated data and model discovery, our solution not only enhances ML outcomes but also establishes an economically sustainable framework for monetizing external data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17843 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Optimal Pricing for Data-Augmented AutoML Marketplaces Han, Minbiao Light, Jonathan Xia, Steven Galhotra, Sainyam Fernandez, Raul Castro Xu, Haifeng Machine Learning Computer Science and Game Theory Organizations often lack sufficient data to effectively train machine learning (ML) models, while others possess valuable data that remains underutilized. Data markets promise to unlock substantial value by matching data suppliers with demand from ML consumers. However, market design involves addressing intricate challenges, including data pricing, fairness, robustness, and strategic behavior. In this paper, we propose a pragmatic data-augmented AutoML market that seamlessly integrates with existing cloud-based AutoML platforms such as Google's Vertex AI and Amazon's SageMaker. Unlike standard AutoML solutions, our design automatically augments buyer-submitted training data with valuable external datasets, pricing the resulting models based on their measurable performance improvements rather than computational costs as the status quo. Our key innovation is a pricing mechanism grounded in the instrumental value - the marginal model quality improvement - of externally sourced data. This approach bypasses direct dataset pricing complexities, mitigates strategic buyer behavior, and accommodates diverse buyer valuations through menu-based options. By integrating automated data and model discovery, our solution not only enhances ML outcomes but also establishes an economically sustainable framework for monetizing external data. |
| title | Optimal Pricing for Data-Augmented AutoML Marketplaces |
| topic | Machine Learning Computer Science and Game Theory |
| url | https://arxiv.org/abs/2310.17843 |