h2o
An open-source, in-memory platform for distributed and scalable machine learning with support for a wide range of algorithms and big data technologies.
What is h2o?
H2O is an open-source, distributed machine learning platform that provides a fast, scalable in-memory environment for building and deploying models. It supports a wide range of algorithms, from deep learning and gradient boosting to automated machine learning (AutoML), and integrates with big data technologies like Hadoop and Spark. The platform addresses the need for efficient, large-scale machine learning workflows accessible through multiple programming languages.
Data scientists, machine learning engineers, and developers who need to build and deploy scalable machine learning models on large datasets, especially those working in big data environments with Hadoop or Spark.
Developers choose H2O for its combination of speed, scalability, and extensive algorithm library in an open-source package. Its ability to integrate with existing big data stacks and support for multiple interfaces (R, Python, Java, etc.) reduces friction in production workflows, while features like AutoML and model export (POJO/MOJO) streamline the end-to-end machine learning process.
Overview
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.
Use Cases
Best For
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
