  Recent advances in Artificial Intelligence, especially in Machine Learning
(ML), have brought applications previously considered as science fiction (e.g.,
virtual personal assistants and autonomous cars) into the reach of millions of
everyday users. Since modern ML technologies like deep learning require
considerable technical expertise and resource to build custom models, reusing
existing models trained by experts has become essential. This is why in the
past year model stores have been introduced, which, similar to mobile app
stores, offer organizations and developers access to pre-trained models and/or
their code to train, evaluate, and predict samples. This paper conducts an
exploratory study on three popular model stores (AWS marketplace, Wolfram
neural net repository, and ModelDepot) that compares the information elements
(features and policies) provided by model stores to those used by the two
popular mobile app stores (Google Play and Apple's App Store). We have found
that the model information elements vary among the different model stores, with
65% elements shared by all three studied stores. Model stores share five
information elements with mobile app stores, while eight elements are unique to
model stores and four elements unique to app stores. Only few models were
available on multiple model stores. Our findings allow to better understand the
differences between ML models and "regular" source code components or
applications, and provide inspiration to identify software engineering
practices (e.g., in requirements and delivery) specific to ML applications.
