# A Novel Methodology For Crowdsourcing AI Models in an Enterprise

arXiv: https://arxiv.org/abs/2103.14033 · HTML: https://mlsanity.com/paper/2103.14033/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [The Deepfake Detection Challenge (DFDC) Preview Dataset](https://mlsanity.com/paper/1910.08854/) (2019)
- [Microsoft Malware Classification Challenge](https://mlsanity.com/paper/1802.10135/) (2018)
- [EvalAI: Towards Better Evaluation Systems for AI Agents](https://mlsanity.com/paper/1902.03570/) (2019)

## Abstract (arXiv)

The evolution of AI is advancing rapidly, creating both challenges and opportunities for industry-community collaboration. In this work, we present a novel methodology aiming to facilitate this collaboration through crowdsourcing of AI models. Concretely, we have implemented a system and a process that any organization can easily adopt to host AI competitions. The system allows them to automatically harvest and evaluate the submitted models against in-house proprietary data and also to incorporate them as reusable services in a product.