The #1 open-source machine learning
platform for the enterprise
Open Source, Distributed Machine Learning
H2O is a fully open-source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning, and more. H2O also has an industry-leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 18,000 organizations globally and is extremely popular in both the R & Python communities.
Key Features ofH2O
How it Works
H2O works on existing big data infrastructure, on bare metal, or top of existing Hadoop or Spark clusters. It can ingest data directly from HDFS, Spark, S3, Azure Data Lake, or any other data source into its in-memory distributed key-value store.
Distributed, In-Memory Machine Learning
H2O takes advantage of the computing power of distributed systems and in-memory computing to accelerate machine learning using its industry parallelized algorithms which take advantage of fine-grained in-memory MapReduce.
When AI becomes mission-critical for enterprise success, H2O.ai is there to help. H2O Enterprise Support provides the services you need to optimize your investments in people and technology to deliver on your AI vision. H2O Enterprise Support includes training, a dedicated account manager, 24/7 support, accelerated issue resolution, and direct enhancement requests. Enterprise support also gives you access to H2O experts in data science, the H2O platform, and DevOps/production deployment to accelerate and expand your adoption of AI. Also, Enterprise Support customers have access to Enterprise Steam or H2O Sparkling Water to deploy and manage models in their Hadoop or Spark clusters.
H2O4GPU is an open-source, GPU-accelerated machine learning package with APIs in Python and R that allows anyone to take advantage of GPUs to build advanced machine learning models. A variety of popular algorithms are available including Gradient Boosting Machines (GBM’s), Generalized Linear Models (GLM’s), and K-Means Clustering. Our benchmarks found that training machine learning models on GPUs were up to 40x faster than CPU based systems.
Featured Use Cases
Introduction to Machine Learning with H2O Tutorial
In this tutorial for the H2O platform, you will learn how to use H2O’s GLM Random Forest, GBM Models, and grid search to tune hyperparameters for a classification problem. We will be using a subset of the Freddie Mac Single-Family dataset to try to predict whether or not a mortgage loan will be delinquent using H2O’s GLM, Random Forest, and GBM models. We will go over how to use these models for classification problems, and we will demonstrate how to use H2O’s grid search to tune the hyper-parameters of each model.