Our primary use case is for data science. Some of our data scientists use it pretty heavily to build models.

Our primary use case is machine learning.

I am a solution architect and a consultant, and I use H2O as a machine learning platform. I create ensemble models using R and H2O, tune the hyperparameters, and then deploy them.

There are various use cases for this solution. One of the ones I worked on was a trailer forecasting solution. The customer wanted to understand the preload capacity that would be needed to have on hand so that they could call upon the right sized trailers and the right packages. It was a problem of logistics where you had to determine how many trailers were required in order to ship the packages being transported, and also have them ready just in time.

We currently use H2O for real-time predictive analytics for fraud prevention. We have a Java-based feature engineering pipeline that attaches to POJO objects obtained from H2O. We run the whole pipeline on lightweight VMs and get under 1ms latency for each real-time transaction scoring.

Testing/modeling data in the initial stages of approaching a machine-learning problem. Environment: Laptops running Ubuntu 16.04/Python 3.

The idea is to migrate the current model's development practice to another platform. Then after, try to create a proprietary platform using R and Python. The company is interested in using an external platform in order to have an updated environment.

We use it for building models with large amounts of data.