There are 2 repositories under h2o topic.
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
Materials for GWU DNSC 6279 and DNSC 6290.
R package for automation of machine learning, forecasting, model evaluation, and model interpretation
Analytics & Machine Learning R Sidekick
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)
Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)
Showcase for using H2O and R for scoring for marketing campaign in retail
A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python
Forecasting with H2O AutoML. Use the H2O Automatic Machine Learning algorithm as a backend for Modeltime Time Series Forecasting.
Mercury-ML is an open source Machine Learning workflow management library. Its core contributors are employees of Alexander Thamm GmbH
NOTE: skutil is now deprecated. See its sister project: https://github.com/tgsmith61591/skoot. Original description: A set of scikit-learn and h2o extension classes (as well as caret classes for python). See more here: https://tgsmith61591.github.io/skutil
Exemplary, annotated machine learning pipeline for any tabular data problem.
Add oauth2 authentication layer with ngx_http_auth_request_module
H2O Open Source Kubernetes operator and a command-line tool to ease deployment (and undeployment) of H2O open-source machine learning platform H2O-3 to Kubernetes.
MLflow-tracking server example with Minio and H2O
Shapley Values with H2O AutoML Example (ML Interpretability)
This repository covers h2o ai based implementations