h2oai/h2o-3

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

What it solves

H2O is an in-memory platform designed for distributed, scalable machine learning. It enables users to perform large-scale data analysis and model training across clusters, overcoming the memory and processing limitations of single-machine setups.

How it works

The platform provides a distributed engine that supports multiple interfaces, including R, Python, Scala, Java, JSON, and a web-based notebook called Flow. It integrates with big data technologies like Hadoop and Spark. H2O implements a wide array of algorithms (such as GLM, Gradient Boosting, Random Forests, and Deep Neural Networks) and includes H2O AutoML for fully automatic machine learning. Models can be exported into POJO or MOJO formats for high-performance scoring in production environments.

Who it’s for

It is built for data scientists and developers who need to scale their machine learning workflows to big data environments and those who want to automate the model selection and tuning process via AutoML.

Highlights

  • Distributed Scaling: Works seamlessly with Hadoop and Spark for large-scale ML.
  • Multi-language Support: Accessible via Python, R, Java, Scala, and JSON.
  • AutoML: Includes a fully automatic machine learning algorithm to simplify model building.
  • Production-Ready: Models can be exported as MOJOs or POJOs for extremely fast scoring.

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