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.

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

Tutorials and training material for the H2O Machine Learning Platform

A curated list of gradient boosting research papers with implementations.

Sparkling Water provides H2O functionality inside Spark cluster

This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies.

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

H2O.ai Machine Learning Interpretability Resources

Presentations from H2O meetups & conferences by the H2O.ai team

A curated list of research, applications and projects built using the H2O Machine Learning platform

Access log profiler based on response time

Materials for GWU DNSC 6279 and DNSC 6290.

Analytics & Machine Learning R Sidekick

R package for automation of machine learning, forecasting, model evaluation, and model interpretation

Comparison tools

Identifying diseases in chest X-rays using convolutional neural networks

Deep Learning UDF for KSQL, the Streaming SQL Engine for Apache Kafka with Elasticsearch Sink Example

ForestFlow is a policy-driven Machine Learning Model Server. It is an LF AI Foundation incubation project.

An End-to-End Implementation of AutoML with H2O, MLflow, FastAPI, and Streamlit for Insurance Cross-Sell

RSparkling: Use H2O Sparkling Water from R (Spark + R + Machine Learning)