Other competition platforms
Though this book focused on competitions on Kaggle, we cannot forget that many data competitions are held on private platforms or on other competitions platforms. In truth, most of the information you will find in this book will hold also for all the other competitions, since they all basically operate under similar principals and the benefits for the participants are more or less the same as Kaggle’s.
Since many other competition platforms are localized in specific countries or are specialized in certain kinds of competitions, for completeness we will briefly introduce some of them, at least those we have some experience and knowledge of.
DrivenData (https://www.drivendata.org/competitions/) is crowdsourcing competition platform devoted to social challenges (see https://www.drivendata.co/blog/intro-to-machine-learning-social-impact/). The company itself is a social enterprise whose aim is to bring data science solutions, thanks to data scientists building algorithms for social good, to organizations tackling the world’s biggest challenges. For instance you an read in this article, https://www.engadget.com/facebook-ai-hate-speech-covid-19-160037191.html, how Facebook has choosen DrivenData for its competition on building models against hate speech and misinformation.
Numerai (https://numer.ai/) is an AI-powered, crowd-sourced hedge fund based in San Francisco which hosts a weekly tournament in which you can submit your predictions on hedge fund obfuscated data and earn your prizes in the company’s crypto currency, Numeraire.
CrowdAnalytix (https://www.crowdanalytix.com/community) a bit less active now, this platform used to host quite a few challenging competitions a short ago, as you can read from this blog post: https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. Also the community blog is quite interesting for having an idea of what challenges you can find on this platform: https://www.crowdanalytix.com/jq/communityBlog/listBlog.html.
Signate (https://signate.jp/competitions) is a Japanese data science competition platform. It is quite rich in contests and it offers a ranking system similar to Kaggle’s one (https://signate.jp/users/rankings).
Zindi (https://zindi.africa/competitions) is a data science competition platform from Africa. It hosts competitions focused on solving Africa’s most pressing social, economic and environmental problems.
Alibaba Cloud (https://www.alibabacloud.com/campaign/tianchi-competitions) is a Chinese cloud computer and AI provider who has launched the Tianchi Academic competitions, partnering with academic conferences such as SIGKDD, IJCAI-PRICAI and CVPR and featuring challenges such as image-based 3D shape retrieval, 3D object reconstruction, or instance segmentation.
Analytics Vidhya (https://datahack.analyticsvidhya.com/) the largest Indian community for data science, offers a platform for data science hackatons.
CodaLab (https://codalab.lri.fr/) is instead a French-based data science competition platform, created as a joint venture between Microsoft and Stanford University in 2013. They feature a similar Kernel feature (here called Worksheets: https://worksheets.codalab.org/) for knowledge sharing and reproducible modeling as Kaggle.
Other minor platforms are CrowdAI (https://www.crowdai.org/) from École Polytechnique Fédérale de Lausanne in Switzerland, InnoCentive (https://www.innocentive.com/), Grand-Challenge (https://grand-challenge.org/) for biomedical imaging, DataFountain (https://www.datafountain.cn/business?lang=en-US), OpenML (https://www.openml.org/) and the list could go on. You can always find a list of many on-running major competitions on the Russian community Open Data Science (https://ods.ai/competitions) and thus discover even new competition platforms from time to time.
The alternatives and opportunities besides Kaggle are quite a lot. The interesting aspect of such an abundance of opportunities is that you can find more easily a competition that could interests you more because of its specialization and data. Also, expect less competitive pressure on these challenges since they are less known and advertized. Also, expect less sharing among participants since no other competition platform up to now has reached the same richness of sharing and networking tools as Kaggle has.
