Optimal Pricing for Data-Augmented AutoML Marketplaces

TL;DR

This paper introduces a practical data-augmented AutoML marketplace that automatically enhances models with external data, using performance-based pricing to improve ML outcomes and create a sustainable data monetization framework.

Contribution

It proposes a novel marketplace design integrating external data augmentation with performance-based pricing, addressing strategic behavior and valuation diversity in AutoML environments.

Findings

Effective performance-based data pricing mechanism

Enhanced ML model quality through external data integration

Sustainable economic framework for data monetization

Abstract

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.