  There has been an unprecedented surge in the number of service providers
offering a wide range of machine learning prediction APIs for tasks such as
image classification, language translation, etc. thereby monetizing the
underlying data and trained models. Typically, a data owner (API provider)
develops a model, often over proprietary data, and leverages the infrastructure
services of a cloud vendor for hosting and serving API requests. Clearly, this
model assumes complete trust between the API Provider and cloud vendor. On the
other hand, a malicious/buggy cloud vendor may copy the APIs and offer an
identical service, under-report model usage metrics, or unfairly discriminate
between different API providers by offering them a nominal share of the
revenue. In this work, we present the design of a blockchain based
decentralized trustless API marketplace that enables all the stakeholders in
the API ecosystem to audit the behavior of the parties without having to trust
a single centralized entity. In particular, our system divides an AI model into
multiple pieces and deploys them among multiple cloud vendors who then
collaboratively execute the APIs. Our design ensures that cloud vendors cannot
collude with each other to steal the combined model, while individual cloud
vendors and clients cannot repudiate their input or model executions.
