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The Netflix Prize competition (2006, completed in 2009) was a Kaggle competition before Kaggle competitions (2010), with the same business-side incentives. Giv

The Netflix Prize competition (2006, completed in 2009) was a Kaggle competition before Kaggle competitions (2010), with the same business-side incentives. Given the meteoric rise of ML/AI in the past few years, I'm surprised that Kaggle doesn't come up more often. It was all the rage 2013-2018...then most I heard about it was that it allowed free access to TPUs.

htrp 3y ago
I feel like kaggle is a copycat wasteland for most projects now. You don't quite get top talent, rather you get a bunch of people xgboosting their way up your leaderboard.
- erhserhdfd 3y ago
  "...rather you get a bunch of people xgboosting their way up your leaderboard" --> I don't see that as a universally bad thing. I feel this is actually very representative of the majority of ML projects that most organizations encounter. Most organizations don't have petabytes of data and a huge compute budget to train a DNN. They typically have megabytes to gigabytes of somewhat crappy data and need something that can be developed and deployed relatively quickly for low cost.
hcarlens 3y ago
There have been quite a few interesting Kaggle competitions in recent years, as well as other interesting ML/data science competitions on other platforms. Platforms like Kaggle, DrivenData, Zindi, AIcrowd, CodaLab and others are running dozens-hundreds of competitions a year in total, including ones linked to top academic conferences. One interesting recent one is this one on LLM efficiency - trying to see to what extent people can fine-tune an LLM with just 1GPU and 24h: https://llm-efficiency-challenge.github.io/ https://llm-efficiency-challenge.github.io/ Or the Makridakis series of challenges, running since the 80s, which are a great testbed for time-series models (the 6th one finished just last year): https://mofc.unic.ac.cy/the-m6-competition/ https://mofc.unic.ac.cy/the-m6-competition/

htrp 3y ago

I feel like kaggle is a copycat wasteland for most projects now. You don't quite get top talent, rather you get a bunch of people xgboosting their way up your leaderboard.

erhserhdfd 3y ago
"...rather you get a bunch of people xgboosting their way up your leaderboard" --> I don't see that as a universally bad thing. I feel this is actually very representative of the majority of ML projects that most organizations encounter. Most organizations don't have petabytes of data and a huge compute budget to train a DNN. They typically have megabytes to gigabytes of somewhat crappy data and need something that can be developed and deployed relatively quickly for low cost.

hcarlens 3y ago

There have been quite a few interesting Kaggle competitions in recent years, as well as other interesting ML/data science competitions on other platforms. Platforms like Kaggle, DrivenData, Zindi, AIcrowd, CodaLab and others are running dozens-hundreds of competitions a year in total, including ones linked to top academic conferences. One interesting recent one is this one on LLM efficiency - trying to see to what extent people can fine-tune an LLM with just 1GPU and 24h: https://llm-efficiency-challenge.github.io/ https://llm-efficiency-challenge.github.io/ Or the Makridakis series of challenges, running since the 80s, which are a great testbed for time-series models (the 6th one finished just last year): https://mofc.unic.ac.cy/the-m6-competition/ https://mofc.unic.ac.cy/the-m6-competition/