Talking Points:

• Real World Example Of H2O Modeling

   

   

Speakers:

• Narrator

Read the Full Transcript

Narrator:

So your business has massive amounts of customer data stored in Hadoop that you need to analyze. The more data you analyze, the better you can predict how to serve your customers. But organizing and analyzing this data requires complicated math and modeling this data to come up with better predictions usually takes days to weeks and is often error prone. So what's the best solution? Quite frankly, it's H2O from Oxdata. The industry's first open source prediction and math engine that will enable you to make better predictions and build accurate models faster. As makers of the paralleled modeling and scoring engine of Hadoop, we got you covered. 

Real World Example Of H2O Modeling

Here's a real world example. Let's say your business is trying to understand the best product placement for optimal customer engagement. You want to model the interactions of your customers on your website to make better predictions on what they want to do next.

Well, H2O allows you to model all of your data with better algorithms using mini machines. This way, you won't have to sample a smaller data set for performance reasons. H2O then allows you to score hundreds of models in nanoseconds and deliver better predictions to your business. The reality is data is messy and so many hours of data science go into moving big files and munging missing features. And that's why we design H2O'S data console to make those tasks a piece of cake within a familiar interface. Simply put, H2O makes big data science well simple. So when you're ready to mine the true gold hidden within your big data, you're ready for H2O.