The encyclopedia · Software & IT · Product decision · 2010–2020
Kaggle turned machine-learning problems into public competitions
Organizations post a data problem and prize; thousands of data scientists compete to build the best model.
Kaggle
the move
Companies accumulate data they want to turn into value, but data scientists are rare and organizations struggle to acquire talent. Kaggle.com offers an alternative: companies host machine-learning competitions and participants build prediction models for prizes.
A 2020 HICSS study used ten expert interviews plus data crawled from Kaggle to understand how organizations use competitions. It found real benefits — discussing with participants and learning state-of-the-art approaches — but noted competitions can only cover a fraction of the tasks in a typical data science project.
The study identified 12 factors within three categories that influence an organization's perceived success when hosting a data science competition, providing a framework for when crowdsourcing beats hiring.
why it works
- The crowd is far larger and more diverse than any in-house team.
- A clear evaluation metric lets organizations compare models objectively.
- Competitions expose internal teams to state-of-the-art approaches.
- Hosting costs a prize, not salaries and headcount.
what transfers
If expertise is scarce and expensive, buy solutions instead of headcount: a well-scoped competition with a clear metric lets the crowd do the work and teaches your team in the process.
what came after
Kaggle became the reference marketplace for data-science competitions, used by companies and researchers worldwide to source models, benchmark approaches and recruit talent, before being acquired by Google in 2017.
references
- Crowdsourcing Data Science: A Qualitative Analysis of Organizations' Usage of Kaggle Competitions
- Crowdsourcing Data Science: A Qualitative Analysis of Organizations' Usage of Kaggle Competitions
- Kaggle Competitions
spotted an error? The archive wants to know.