Algorithms developed from the ground up for distributed computing and for both supervised and unsupervised approaches including Random Forest, GLM, GBM, XGBoost, GLRM, Word2Vec and many more.

Use the programing language you already know like R, Python and others to build models in H2O, or use H2O Flow, a graphical notebook based interactive user interface that does not require any coding.

H2O’s AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time limit. Stacked Ensembles will be automatically trained on collections of individual models to produce highly predictive ensemble models which, in most cases, will be the top performing models in the AutoML Leaderboard.

In-memory processing with fast serialization between nodes and clusters to support massive datasets. Distributed processing on big data delivers speeds up to 100x faster with fine-grain parallelism, enabling optimal efficiency without introducing degradation in computational accuracy.

Easy to deploy POJOs and MOJOs to deploy models for fast and accurate scoring in any environment, including very large models.