Kaggle competition platform
Other companies than Netflix indeed benefitted from data science competitions. The list is indeed long but we can quote a few examples where the company holding the competition reported a clear benefit from it. For instance, we can quote the insurance company AllState that could improve its actuarial models built by their experts thanks to a competition involving hundreds of data scientists (https://www.kaggle.com/c/ClaimPredictionChallenge). As another well documented example, we can also mention about General Electric that could improve by 40% the industry standard on predicting arrival times of airline flights thanks to an analogue competition (https://www.kaggle.com/c/flight). Both these competitions were held on the Kaggle competition platform.
The Kaggle competition platform has until now held hundreds of competitions and these two are just a couple of examples of companies that successfully used its competitions to boost their own models and analytics efforts. Let’s take a step back from specific competitions for a moment and let’s talk about the Kaggle company, which is the common thread of all this book.
Kaggle took its first steps in February 2010 thanks to the idea of Anthony Goldbloom, an Australian trained economist (he has a degree in Economics and Econometrics from Melbourne University). After working at Australia's Department of Treasury and in the Research department at the Reserve Bank of Australia, Goldbloom worked in London as an intern at The Economist, the international weekly newspaper on current affairs, international business, politics, and technology. At The Economist he had occasion to write an article about big data that inspired his idea of building a competition platform that could crowdsource the best analytical experts in solving interesting machine learning problems (https://www.smh.com.au/technology/from-bondi-to-the-big-bucks-the-28yearold-whos-making-data-science-a-sport-20111104-1myq1.html). Since the crowdsourcing dynamics had a relevant part in the business idea for this platform, he derived the name Kaggle, which recalls by rhyme the term “gaggle” i.e. a flock of geese (the goose is also the symbol of the platform).
After moving to the Silicon Valley in the USA, his Kaggle start-up received $11.25 million in Series A funding from a round led by Khosla Ventures and Index Ventures, two quite renown venture capital firms. First competitions rolled out, the community grew and some of the initial competitors came to become quite prominent, such as Jeremy Howards, the Australian data scientist and entrepreneur, who, after winning a couple of competitions on Kaggle, become the President and Chief Scientist of the company. Jeremy Howard left his position as President in December 2013 and thereafter he started a new start-up, fast.ai (www.fast.ai), offering machine learning courses and a deep learning library for coders.
At the times there were other prominent Kagglers (the name to indicate frequent participants to competitions held by Kaggle) such as Jeremy Achin and Thomas de Godoy. After reaching the top 20 global rankings on the platform, they promptly decided to retire and to found their own company, DataRobot. They soon after started hiring their best employers among the participants in the Kaggle competitions in order to instill the best machine learning knowledge and practice into the software they were developing. Today DataRobot is an undoubted leader in autoML (automatic machine learning).
The Kaggle competitions claimed more and more attention from a larger audience and even Geoffrey Hinton, the Godfather of deep learning participated (and won) in a Kaggle competition hosted by Merck in 2012 (https://www.kaggle.com/c/MerckActivity/overview/winners). Kaggle has also been the platform where Francois Chollet launched his deep learning package Keras during the Otto Group Product Classification Challenge (https://www.kaggle.com/c/otto-group-product-classification-challenge/discussion/13632) and Tianqi Chen launched XGBoost, a speedier and more accurate version of the gradient boosting machines, in the Higgs Boson Machine Learning Challenge (https://www.kaggle.com/c/higgs-boson/discussion/10335).
Competition after competition the community revolving around Kaggle grew to touch one million in 2017, the same year as, during her keynote at Google Next, Fei-Fei Li, Chief Scientist at Google, announced that Google Alphabet was going to acquire Kaggle. Since then Kaggle has become part of Google.
Today the Kaggle community is still active and growing. It has offered to many of his participants opportunities to create their own company, to launch machine learning software and packages, to get interviews on magazines (https://www.wired.com/story/solve-these-tough-data-problems-and-watch-job-offers-roll-in/), to arrange a course on Coursera (https://www.coursera.org/learn/competitive-data-science), to write machine learning books (https://twitter.com/antgoldbloom/status/745662719588589568), to find their dream job and, most important, or just to learn more about skills and technicalities about data science.
