Yandex open sources CatBoost

Artificial intelligence is now powering a growing number of computing functions, and today the developer community today is getting another AI boost, courtesy of Yandex. Today, the Russian search giant — which, like its US counterpart Google, has extended into a myriad of other business lines, from mobile to maps and more — announced the launch of CatBoost, an open source machine learning library based on gradient boosting — the branch of ML that is specifically designed to help “teach” systems when you have a very sparse amount of data, and especially when the data may not all be sensorial (such as audio, text or imagery), but includes transactional or historical data, too .
CatBoost is making its debut in two ways today. (I think ‘Cat’, by the way, is a shortening of ‘category’, not your feline friend, although Yandex is enjoying the play on words. If you visit the CatBoost site you will see what I mean.)
First, Yandex says that it is starting to use the new framework itself across its own services, to replace MatrixNet, which is the machine learning algorithm that up to now has been used at the company for everything, from ranking tasks, weather forecasting, Yandex.taxi services (which are now being spun off into a $3.7 billion joint venture with Uber across Russian markets) and recommendations. The switchover from MatrixNet to CatBoost is happening now and will continue in the months ahead .
Second, Yandex is offering the CatBoost library as a free service, released under an Apache license, to any and all who need or want to use gradient-boosting tech in their own programs. “This is the pinnacle of a lot of years of work,” Misha Bilenko, Yandex’s head of machine intelligence and research said in an interview. “We have been using a lot of open source machine learning tools ourselves, so it’s good karma to give something back.” He mentioned Google’s move to open source Tensorflow back in 2015 and the establishment and growth of Linux as two inspirations here rent handbags.