Making the case for PMML and ADAPA

If you are not familiar with PMML, the Predictive Model Markup Language, you may be wondering what all the fuss is about ...

PMML is the de facto standard to represent data mining and predictive analytic solutions. With PMML, one can easily share a predictive solution among PMML-compliant applications and systems  For example, you can build your model in R, export it in PMML, and use ADAPA, the Zementis Scoring Engine, to deploy it in production.

Many data mining models are a one-time affair. You use historical data to build the model and use it to analyze ... historical data. Wait! That sounds more like descriptive analytics, not predictive analytics. Well, that is sort of true. To be truly predictive, a data mining model needs to be applied to new data. These are the models that need to be operationally deployed and, from my point of view, these are the solutions that are truly revolutionizing the way we do business and live in the Big Data world.

If you want then to use your data mining model to make predictions when presented with new data, it needs to be a dynamic asset. It cannot be static. You need to be able to build it and instantly put it to use. And, that's where PMML and ADAPA come in handy.

Obviously, a few data mining tools try to lock you in. You happily build the model using tool A, just to realize that you need the same tool to execute it. In this case, you are missing out. Here are some of the benefits of moving your predictive model to ADAPA:

• Overcome speed/memory limitations
• Dramatically lower your infrastructure cost
• Tap into all the advantages of cloud computing with ADAPA on the Cloud (IBM SmartCloud or Amazon EC2)
• Produce scores in real-time (using Web Services or Java API), on-demand, or batch-mode
• Execute your models directly from Excel, by using the ADAPA Add-in for Excel
• Benefit from using a set of PMML-compliant model development tools (best of breed)
• Deploy your models in minutes
• Manage models via Web Services or a Web console
• Upload one or many models into ADAPA at once
• Benefit from the seamless integration of business rules and predictive models (yes, for those who need it, ADAPA comes with a business rules engine)

PMML and ADAPA allow you to use best of breed tools (not the same old tool) for the job at hand. Also, you can leverage the expertise from a diverse group of data scientists. That means, not all your data scientists need to be experts on a single tool. They can use different tools that share one thing in common, the PMML standard. And, once represented in PMML, models can be easily understood by all team members. PMML allows for transparency and, in doing so, fosters best practices.

Why not benefit from: 1) an open standard to represent data mining models; and 2) a proven scoring engine that consumes any version of PMML and make it available for execution right away, in real-time?

Keep also in mind that ADAPA's sister product, the Universal PMML Plug-in (UPPI), allows you to move the same PMML file in-database or Hadoop. UPPI is currently available for EMC Greenplum, SAP Sybase IQ, IBM Netezza, and Teradata/Aster. With UPPI for in-database scoring, there is no need to move your data outside the database. Data and models reside inside it and so there is minimal data movement and maximum scoring speed. UPPI is also available for Datameer and will soon be available for Hadoop/Hive.

Making a model operational in minutes has never been easier! And, it is all because of PMML and scoring tools such as ADAPA and UPPI.

Zementis is proud to announce PMML 4.1 support

PMML 4.1, the latest version of the Predictive Model Markup Language, is loaded with new and powerful features. 

Zementis is proud to announce support for PMML 4.1 throughout its scoring products, including:

• ADAPA on Site
• ADAPA on the Cloud (Amazon and IBM SmartCloud)
• UPPI for in-database scoring (IBM Netezza, SAP Sybase IQ, EMC Greenplum) 
• UPPI for Hadoop (Datameer).

We have also updated our PMML conversion process so that it now converts PMML files from older versions to version 4.1. In this way, every time a PMML file is presented to ADAPA or UPPI, it is automatically converted to PMML 4.1.

  

Our support for PMML 4.1 includes:

1) Scorecards (including reason or adverse codes and point allocation for complex attributes)

2) Post-processing: you can now transform scores into business decisions as well as output generic data manipulation steps

3) Multiple Models: a powerful and yet simpler way for the expression of model segmentation, composition, chaining and ensemble, which includes Random Forest models

4) Is the model scorable? The "isScorable" flag was added as a way to flag models not destined for production deployment, but that are nonetheless an important part of the model building cycle

5) New built-in functions (for pre- and post-processing).

With this new release and version update, ADAPA and UPPI can be used not only for deployment and execution of predictive solutions, but also for data analysis and processing before model training.

  

If you have any questions about PMML 4.1 and all the features supported in our products, please make sure to contact us or feel free to check out our PMML 4.1 forum for detailed support information.

Universal PMML Plug-in for EMC Greenplum Database

It is our pleasure to announce a new Zementis product, the Universal PMML Plug-in for in-database scoring. Available now for the EMC Greenplum Database, a high-performance massively parallel processing (MPP) database, the plug-in leverages the Predictive Model Markup Language (PMML) to execute predictive models directly within EMC Greenplum, for highly optimized in-database scoring.
Developed by the Data Mining Group (DMG), PMML is supported by all major data mining vendors, e.g., IBM SPSS, SAS, Teradata, FICO, STASTICA, Microstrategy, TIBCO and Revolution Analytics as well as open source tools like R, KNIME and RapidMiner. With PMML, models built in any of these data mining tools can now instantly be deployed in the EMC Greenplum database. The net result is the ability to leverage the power of standards-based predictive analytics on a massive scale, right where the data resides.
"By partnering with Zementis, a true PMML innovator, we are able to offer a vendor-agnostic solution for moving enterprise-level predictive analytics into the database execution environment," said Dr. Steven Hillion, Vice President of Analytics at EMC Greenplum. "With Zementis and PMML, the de-facto standard for representing data mining models, we are eliminating the need to recode predictive analytic models in order to deploy them within our database. In turn, this enables an analyst to reduce the time to insight required in most businesses today."

Want to learn more?

To learn more about how the EMC Greenplum Database and the Universal PMML Plug-in work together, feel free to:

• Visit the PMML Plug-in product page
• Download the white paper

The Universal PMML Plug-in for the EMC Greenplum Database is available now. Contact us today for more information.