Yellowfin 7.4: Enabling Data Science across the Enterprise with H2O.ai

Yellowfin 7.4: Enabling Data Science across the Enterprise with H2O.ai

In our previous data science article, we introduced the main problem faced in bridging data science to production in BI, and some of the solutions Yellowfin 7.4 brings to the table.

The article also introduced Yellowfin 7.4’s support for PMML in providing self-contained model outputs to be dropped from popular tools straight into Yellowfin and running them over report data in real-time.

If you’ve not read it, we highly encourage going over that first and coming back to this blog.

 

H2O.ai

For today’s piece, we’ll explore the same framework that also allows the integration of proprietary or open source API-based data science capabilities. An example of this is H2O.ai support in Yellowfin 7.4.

H2O.ai is arguably the world’s leading open source AI & Deep Learning platform.

As you can see from their website, they boast of high adoption rates from global organizations and data scientists. Their solutions also range from Machine Learning APIs, Sparkling Water (enterprise grade machine learning) to Deep Water (GPU-enabled Deep Learning).

You can also download the latest stable release of H2O.ai here.

 

How does it work with Yellowfin 7.4?

In Yellowfin 7.4, H2O.ai is supported in both Report Advanced Functions and in our new Data Transformation module.

 

Advanced Functions

After an initial query to retrieve data, Yellowfin allows post-processing calculations to be applied to query results and transform them; we call them Advanced Functions. It’s an open framework that allows our customers to bring specific post-query column operations into the platform.

For Advanced Functions, this integration enables you to connect to H2O.ai’s API via Model Optimized Java Objects (MOJO) and apply lightning fast algorithms over post-query data.

 

Data Transformation

In addition to this, we have also enabled this functionality in Yellowfin 7.4’s Data Transformation module as a Transform Step, so you can run all of H2O.ai’s algorithms over your production data as part of a data flow.

For example, you can drop H2O.ai’s models and predictions into a Transformation Flow and save those predictions into a table somewhere, or even drop a Report (with H2O.ai APIs applied via Advanced Functions) as an Input Step within a Transformation Flow.

However, it’s much easier to show it in action. Check out the video below for a behind-the-scenes look at how H2O.ai capabilities integrate with Yellowfin 7.4:

To find out more about Yellowfin 7.4, please visit our What’s New page here.

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