  Recent advances in artificial intelligence research have led to a profusion
of studies that apply deep learning to problems in image analysis and natural
language processing among others. Additionally, the availability of open-source
computational frameworks has lowered the barriers to implementing
state-of-the-art methods across multiple domains. Albeit leading to major
performance breakthroughs in some tasks, effective dissemination of deep
learning algorithms remains challenging, inhibiting reproducibility and
benchmarking studies, impeding further validation, and ultimately hindering
their effectiveness in the cumulative scientific progress. In developing a
platform for sharing research outputs, we present ModelHub.AI
(www.modelhub.ai), a community-driven container-based software engine and
platform for the structured dissemination of deep learning models. For
contributors, the engine controls data flow throughout the inference cycle,
while the contributor-facing standard template exposes model-specific functions
including inference, as well as pre- and post-processing. Python and RESTful
Application programming interfaces (APIs) enable users to interact with models
hosted on ModelHub.AI and allows both researchers and developers to utilize
models out-of-the-box. ModelHub.AI is domain-, data-, and framework-agnostic,
catering to different workflows and contributors' preferences.

Machine Learning;Image and Video Processing