h2o-3: a distributed in-memory platform for scalable machine learning and automated model building

What it solves

H2O provides a distributed, scalable, in-memory platform for machine learning, allowing users to handle large datasets and complex models across clusters of machines. It simplifies the process of building, training, and deploying machine learning models through a variety of interfaces and automated tools.

How it works

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

Who it’s for

Data scientists and developers who need to perform scalable machine learning on large datasets, as well as those who prefer using familiar languages like Python or R while leveraging distributed computing power.

Highlights

• Distributed Scalability: Built for in-memory, distributed machine learning that works with Hadoop and Spark.
• Broad Algorithm Support: Includes GLM, XGBoost, Random Forests, Deep Neural Networks, Naive Bayes, and more.
• AutoML: Features a fully automatic machine learning algorithm to streamline model selection and tuning.
• Production-Ready Export: Models can be exported to POJO or MOJO formats for extremely fast scoring in production environments.
• Multi-Language Support: Accessible via Python, R, Java, Scala, and a web interface.