Академия Яндекса: CatBoost for Spark - видео HD

CatBoost is a popular machine learning library that uses gradient boosted decision trees models. It allows to train models on tabular data with different kinds of features: numeric, categorical, and textual, as well as embeddings, while providing good quality even with default parameters.
It is developed primarily by researchers and engineers of Yandex, the largest IT company of Russia, and is used for search, recommendation systems, personal assistant, self-driving cars, weather prediction and many other tasks at Yandex and in other companies.

In this presentation, we introduce CatBoost distributed training on Spark.

We will discuss the key features, the overall architecture and also present some benchmarks.

Join our talk, if you:
• have a lot of data on hand;
• are using or planning to start using Spark clusters for data processing;
• need to use distributed training for your tasks

Keep in touch by following us on twitter: twitter.com/CatBoostML or chat on Telegram: — t.me/catboost_en — t.me/catboost_ru

CatBoost website: catboost.ai/
CatBoost documentation: catboost.ai/docs
CatBoost on GitHub: github.com/catboost
CatBoost for Apache Spark home: github.com/catboost/catboost/tree/master/catboost/spark/catboost4j-spark