# ModelHub.AI: Dissemination Platform for Deep Learning Models

arXiv: https://arxiv.org/abs/1911.13218 · HTML: https://mlsanity.com/paper/1911.13218/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Management of Machine Learning Lifecycle Artifacts: A Survey](https://mlsanity.com/paper/2210.11831/) (2022)
- [A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions](https://mlsanity.com/paper/2103.03610/) (2021)
- [Meta-repository of screening mammography classifiers](https://mlsanity.com/paper/2108.04800/) (2021)
- [The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data](https://mlsanity.com/paper/2205.07516/) (2022)
- [Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study](https://mlsanity.com/paper/2211.02092/) (2022)

## Abstract (arXiv)

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.