Skip to main content
Big Data Test Infrastructure (BDTI)

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.



Find below some of the main features of

  • Leading Algorithms (Algorithms for distributed computing and for both supervised and unsupervised approaches)
  • AutoML (H2O’s AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models)
  • Distributed, In-Memory Processing
  • Simple Deployment
  • Built models with programing languages you already know such as R, Python, and more…
  • Built models with H20 FlOW, graphical notebook based interactive user interface that does not require any coding


H2O Flow is an open source user interface for H2O. It is a web-based interactive environment that allows you to combine code execution, text, mathematics, plots, and rich media in a single document.

With H2O Flow, you can capture, rerun, annotate, present, and share your workflow. H2O Flow allows you to use H2O interactively to import files, build models, and iteratively improve them. Based on your models, you can make predictions and add rich text to create vignettes of your work - all within Flow’s browser-based environment.

Rather than displaying output as plain text, H20 Flow provides a user interface for every H2O operation. It allows you to access any H2O object in the form of well-organized tabular data.

H2Oai flow

Use Cases

Find below some examples of possible use cases:

  • Advanced Analytics
  • Fraud or Anomoly Detection
  • Churn Prediction
  • Credit Scoring
  • Claims Management
  • Digital Advertising

Please find more use case examples here.


Find below some interesting links providing more information on