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.
Automate algorithm selection, feature generation, hyperparameter tuning, iterative modeling, and model assessment. AutoML tools make it easy to train and evaluate machine learning models, allowing people to focus on data and business problems.
A web-based interactive environment that allows you to combine code execution, text, mathematics, plots, and rich media in a single document.
Easy to deploy POJOs and MOJOs to deploy models for fast and accurate scoring in any environment, including very large models.
In-memory processing with fast serialization between nodes and clusters to support massive datasets.
Big Data ready
Distributed processing on Big Data delivers speeds up to 100x faster with fine-grain parallelism, enabling optimal efficiency without introducing degradation in computational accuracy.
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