What is the difference between H2O-3 and H2O-3 Secure?

H2O-3 is the open-source distributed ML and AutoML engine under Apache 2.0. H2O-3 Secure is the same code with enterprise packages, MOJO scoring into Hadoop, Spark, Teradata, and Oracle, AutoDoc, SSO and RBAC, controls aligned to the FedRAMP High baseline, CVE patching, and support with SLAs.

Does open-source H2O-3 include AutoDoc?

No. AutoDoc ships with H2O-3 Secure and with Driverless AI. It is the main reason teams move from open-source H2O-3 to Secure when a model reaches model risk review.

Do I need to change my code to move to H2O-3 Secure?

No. H2O-3 Secure runs the same algorithms and APIs as open source in Python, R, Java, and Scala. Existing scripts, AutoML runs, and Sparkling Water jobs run unchanged.

What is moving from open-source H2O-3 to H2O-3 Secure?

Starting with release 3.46.0.12, the MOJO runtime, Hadoop, Kubernetes deployment, and Sparkling Water leave the open-source distribution and move to H2O-3 Secure, where they remain fully supported.

Does H2O-3 collect telemetry?

Only if you opt in. Telemetry is off by default and respects DO_NOT_TRACK=1. The full list of what is and is not collected is in the repo README.

Is H2O-3 Secure FedRAMP certified?

H2O-3 Secure ships controls aligned to the FedRAMP High baseline. The FedRAMP Certified offering is H2O AI Cloud for Government, at Class D (High). Alignment is not certification.

H2O-3 and H2O-3 Secure: Open-Source and Enterprise ML | H2O.ai

H2O-3

The open-source distributed ML and AutoML engine that 20,000+ organizations and 2M+ data scientists run. Available in two offerings: free and open source under the Apache 2.0 license, or H2O-3 Secure, the same code with enterprise controls, AutoDoc, and support to run it in production.

Request a demo Documentation

The record

20,000+organizations on H2O open source

2M+data scientists in the community

2017H2O-3 AutoML shipped

Apache 2.0the license of the open-source core

Key takeaways

H2O-3 Secure is the same code and APIs as open-source H2O-3, plus enterprise packages, MOJO scoring, AutoDoc, SSO/RBAC, controls aligned to the FedRAMP High baseline, CVE patching, and SLAs.
Open-source H2O-3 is Apache 2.0 and used by 20,000+ organizations and 2M+ data scientists; it does not include AutoDoc.
AutoDoc is the upgrade lever from open source to Secure: the documentation model risk asks for, generated with the model.
Nothing you built on H2O-3 changes; Secure adds controls around it.

Two offerings

One engine, two ways to run it.

Choose the offering that meets your needs. One codebase, different ways to deploy.

Open source

H2O-3Designed for data scientists and experimentation.Get it on GitHub

EnterpriseRecommended

H2O-3 SecureDesigned for the exacting requirements of commercial use.

Request a demo

Open source ML

Machine learning for everyone.

PythonWorks with pandas, scikit-learn, and Jupyter. Install with pip install h2o.
RWorks with the R ecosystem you know. Install with install.packages("h2o").
Java / ScalaRun H2O directly on the JVM and integrate into existing JVM applications.
H2O FlowA browser-based notebook UI that runs on top of H2O-3. No coding required.

Quick start

Train your first AutoML model in minutes.

Follow these simple steps to install H2O-3 and train your first model.

01Install H2O-3
Bash / ShellDownload
```
pip install h2o
```

02Train your first AutoML model

Python

import h2o
from h2o.automl import H2OAutoML

h2o.init()
data = h2o.import_file("your_data.csv")
aml = H2OAutoML(max_runtime_secs=60, seed=1)
aml.train(y="target_column", training_frame=data)
print(aml.leaderboard)

Why update to H2O-3 Secure

What Secure adds to open source.

H2O-3 open source is for experimentation and research. H2O-3 Secure is the same engine with the packaging an enterprise needs to bet production on it.

Hardened and patchedControls aligned to the FedRAMP High baseline, CVE patching on a committed cadence, and security bulletins you can hand to your CISO.
SSO and RBACEnterprise identity, role-based access, and an audit of who ran what. The controls open-source H2O-3 does not ship.
AutoDocModel documentation for H2O-3 models, generated with the model, in the format a reviewer asks for. The upgrade lever from OSS.

Compare

H2O-3 OSS vs H2O-3 Secure

FeatureH2O-3 OSSH2O-3 Secure

Included in both

All H2O-3 algorithms

How you use it

From notebook to production without a rewrite.

01Keep the codeYour H2O-3 scripts, AutoML runs, and Sparkling Water jobs run unchanged, exactly as they do today.
02Add the controlsSwitch on SSO, RBAC, and the hardened packages, and enrol in CVE patching on a committed cadence.
03Document and scoreAutoDoc for the reviewer. MOJO into the database or Spark cluster where the data already lives.
04Run it with supportSLAs, security bulletins, and H2O AI support engineers on call from the team that builds H2O-3.

Where it fits

A training engine in the predictive lane, and the open-source root of the platform.

H2O-3 sits beside Driverless AI as a training engine, and the Data Science Agent runs it in Notebook Labs. Highlighted is H2O-3.

Details

What ships today

AlgorithmsGLM, GBM, XGBoost, Random Forest, Deep Learning, Stacked Ensembles, K-Means, PCA, Isolation Forest, Word2Vec, and more

AutoMLH2O AutoML: automatic training and tuning with a leaderboard

PlatformsHadoop, Spark (Sparkling Water), Kubernetes, standalone

Run it anywhere

You operate it, or H2O AI operates it for you.

Same platform either way: the same capabilities, the same governance, and the same audit trail. The choice stays open, so you can switch between them as the business changes, without rebuilding what runs on top.

We run itH2O AI Managed Cloud (HAMC)A dedicated, single-tenant cloud that H2O AI provisions, operates, and secures, across fault-tolerant availability zones in the region you choose. Your data never leaves it. Zero-trust access, encrypted in transit and at rest, constantly monitored.AWSAzureGoogle CloudSOC 2 Type 2 · HIPAA / HITECHLearn more

Case studies