  Data analytics using machine learning (ML) has become ubiquitous in science,
business intelligence, journalism and many other domains. While a lot of work
focuses on reducing the training cost, inference runtime and storage cost of ML
models, little work studies how to reduce the cost of data acquisition, which
potentially leads to a loss of sellers' revenue and buyers' affordability and
efficiency.
  In this paper, we propose a model-based pricing (MBP) framework, which
instead of pricing the data, directly prices ML model instances. We first
formally describe the desired properties of the MBP framework, with a focus on
avoiding arbitrage. Next, we show a concrete realization of the MBP framework
via a noise injection approach, which provably satisfies the desired formal
properties. Based on the proposed framework, we then provide algorithmic
solutions on how the seller can assign prices to models under different market
scenarios (such as to maximize revenue). Finally, we conduct extensive
experiments, which validate that the MBP framework can provide high revenue to
the seller, high affordability to the buyer, and also operate on low runtime
cost.
