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H2O has recently released its steam AI engine, a fully open source engine that support the management and deployment of machine learning models. Both H2O on R and H2O steam are easy to set up and use. And both complement each other perfectly.
A very simple example
Use H2O on R to create some predictive models. Well, due to lack of inspiration I just used the iris set to create some binary classifiers.
Once these models are trained, they are available for use in the H2O steam engine. A nice web interface allows you to set up a project in H2O steam to manage and display summary information of the models.
In H2O steam you can select a model that you want to deploy. It becomes a service with a REST API, a page is created to test the service.
And that is it! Your predictive model is up and running and waiting to be called from any application that can make REST API calls.
There is a lot more to explore in H2O steam, but be careful H2O steam is very hot!
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