H2O
Platform
100% open source, fully distributed
in-memory ML with linear scalability
-
Open Source Freedom
Written in Java. Integrates natively with Apache Hadoop® and Apache Spark™.
-
H2O Flow — No-Code GUI
Notebook-style web interface. Import, join, split, model, evaluate — zero code.
-
Universal Data Sources
HDFS, S3, SQL, NoSQL. Works with Excel, R Studio, and Tableau.
-
Massively Scalable Joins
7× faster than R data.table. Linearly scales to 10B × 10B row joins.
-
Real-Time Scoring
Deploy via POJO, MOJO, or REST API in any environment instantly.
Quick Start
curl https://h2o.ai/downloads → unzip
java -jar h2o.jar
open http://localhost:54321
Distributed Algorithms
Built-in ML Library
Supervised Learning
Generalized Linear Models
Naïve Bayes
Distributed Random Forest
Gradient Boosting Machine
Deep Learning
Deep Neural Networks
AutoML
Word2Vec
Ensembles
Unsupervised Learning
K-Means Clustering
Principal Component Analysis
Generalized Low Rank Models
Autoencoders
Anomaly Detection
H2O Compute Pipeline
Load
Data
›
In-Memory
Compression
›
Feature
Engineering
›
Model
Training
›
Evaluate &
Select
›
Score &
Deploy
Advanced Capabilities
Built for Data Scientists
AutoML & Grid Search
Automatic hyperparameter optimization. Train and rank multiple models in one call.
Cross-Validation
Built-in k-fold cross-validation with early stopping based on holdout performance.
Variable Importance
Visualize and interpret model decisions in human-readable format at scale.
Adaptive Learning
Automatic standardization, weight initialization, and adaptive learning rates for deep nets.
POJO / MOJO Export
Export models as portable Java objects. Score in any JVM environment with zero dependencies.
Missing Data Handling
Automatic handling of categorical and missing data — no manual imputation required.
Interfaces & Integrations
Python
R
Java
Scala
JSON
REST API
Apache Spark
Hadoop
Tableau
Excel
AWS
Azure
GCP