Why You Should Consider H2O.ai for Your Machine Learning Stack

When we talk about machine learning tools, the triad Tensor Flow,  Scikit-Learn and Spark MLLib. immediately comes to mind.

However, there’s a tool that has been discreetly gaining ground: H2O.ai.

This tool was originally born in 2011 with its team having the primary objective of democratizing and making machine learning scalable through a more visual platform that offers a good user experience, regardless of technical level.

Some characteristics of H2O.ai:

• Open-Source platform in Java
• Many on the shelf models such as regression models, ensemble, Deep Learning, PCA, etc.
• Extensive documentation on the solution
• Tutorials and more usage tutorials
• Native REST API
• Integration with R and Python
• Integration with Spark
• Distributed ML model processing
• Web interface via Flow GUI
• Can be used standalone on a desktop or via a cluster
• Active community discussing and improving the solution
• Datasource agnostic support
• Model deployment via plain-old Java objects (POJO)

Below are some videos of H2O.ai in action:

H2O.ai for fraud detection

http://www.youtube.com/watch?v=RqkheMI3Ciw

Customer Churn using H2O.ai http://www.youtube.com/watch?v=-u–LeFltk4

Main discussion forum

Github Repository

https://github.com/h2oai

In future posts, we will discuss architectural matters and dive into some tutorials.