  Recent advances in Artificial Intelligence (AI), especially in Machine
Learning (ML), have introduced various practical applications (e.g., virtual
personal assistants and autonomous cars) that enhance the experience of
everyday users. However, modern ML technologies like Deep Learning require
considerable technical expertise and resources to develop, train and deploy
such models, making effective reuse of the ML models a necessity. Such
discovery and reuse by practitioners and researchers are being addressed by
public ML package repositories, which bundle up pre-trained models into
packages for publication. Since such repositories are a recent phenomenon,
there is no empirical data on their current state and challenges. Hence, this
paper conducts an exploratory study that analyzes the structure and contents of
two popular ML package repositories, TFHub and PyTorch Hub, comparing their
information elements (features and policies), package organization, package
manager functionalities and usage contexts against popular software package
repositories (npm, PyPI, and CRAN). Through these studies, we have identified
unique SE practices and challenges for sharing ML packages. These findings and
implications would be useful for data scientists, researchers and software
developers who intend to use these shared ML packages.
