Online PMML Course @ UCSD Extension: Register today!

The Predictive Model Markup Language (PMML) standard is touted as the standard for predictive analytics and data mining models. It is allows for predictive models built in one application to be moved to another without any re-coding. PMML has become the imperative for companies wanting to extract value and insight from Big Data. In the Big Data era, the agile deployment of predictive models is imperative. Given the volume and velocity associated with Big Data, one cannot spend weeks or months re-coding a predictive model into the IT operational environment where it actually produces value (the fourth V in Big Data).

Also, as predictive models become more complex through the use of random forest models, model ensembles, and deep learning neural networks, PMML becomes even more relevant since model recoding is simply not an option.

Zementis has paired up with UCSD Extension to offer the first online PMML course. This is a great opportunity for individuals and companies alike to master PMML so that they can muster their predictive analytics resources around a single standard and in doing so, benefit from all it can offer.

http://extension.ucsd.edu/studyarea/index.cfm?vAction=singleCourse&vCourse=CSE-41184

Course Benefits

• Learn how to represent an entire data mining solution using open-standards
• Understand how to use PMML effectively as a vehicle for model logging, versioning and deployment
• Identify and correct issues with PMML code as well as add missing computations to auto-generated PMML code

Course Dates

07/14/14 - 08/25/14

PMML is supported by most commercial and open-source data mining tools. Companies and tools that support PMML include IBM SPSS, SAS, R, SAP KXEN, Zementis, KNIME, RapidMiner, FICO, StatSoft, Angoss, Microstrategy ... The standard itself is very mature and its latest release is version 4.2.

For more details about PMML, please visit the Zementis PMML Resources page.

Scoring Data from MySQL or SQL Server using KNIME and ADAPA

The video below shows the use of KNIME for handling data (reading data from a flat file and/or a database) as well as model building (data massaging and training a neural network). It also highlights how easy and straightforward it is to move a predictive model represented in PMML, the Predictive Model Markup Language, into the Zementis ADAPA Scoring Engine. ADAPA is then used for model deployment and scoring. PMML is the de facto standard to represent data mining models. It allows for predictive models to be moved between applications and systems without the need for model re-coding.

When training a model, scientists rely on historical data, but when using the model on a regular basis, the model is moved or deployed in production where it presented with new data. ADAPA provides a scalable and blazing fast scoring engine for models in production. And, although KNIME data mining nodes are typically used by scientists to build models, its database and REST nodes nodes can simply be used to create a flow for reading data from a database (MySQL, SQL Server, Oracle, ...) and passing it for scoring in ADAPA via its REST API.

 

Use-cases are:

1. Read data from a flat file, use KNIME for data pre-processing and building of a neural network model. Export the entire predictive workflow as a PMML file and then take this PMML file and upload and score it in ADAPA via its Admin Web Console. 
2. Read data from a database (MySQL, SQLServer, Oracle, ...), build model in KNIME, export model as a PMML file and deploy it in ADAPA using its REST API. This use-case also shows new or testing data flowing from the database and into ADAPA for scoring via a sequence of KNIME nodes. The video also shows a case in which one can use KNIME nodes to simply read a PMML file produced in any PMML-compliant data mining tool (R, SAS EM, SPSS, ...), upload it in ADAPA using the REST API and score new data from MySQL in ADAPA also through the REST interface. Note that in this case, the model has already been trained and we are just using KNIME to deploy the existing PMML file in ADAPA for scoring.

 

Zementis and SAP HANA: Real-time Scoring for Big Data

The Zementis partnership with SAP is manifesting itself in a number of ways. Two weeks ago we were part of the SAP Big Data Bus parked outside Wells Fargo in San Francisco. This week, we would like to share with you three new developments.

1) ADAPA is not being offered at the SAP HANA Marketplace.

2) An interview with our CEO, Mike Zeller, was just featured by SAP on the SAP Blogs.


3) Zementis was again part of the SAP Big Data Bus and the "Big Data Theatre". This time, the bus was parked outside US Bank in Englewood, Colorado. We were engaged in a myriad of conversations with the many people that came through the bus about how ADAPA and SAP HANA work together to bring predictive analytics and real-time scoring to transactional data and millions of accounts, in any industry.

Visit the Zementis ADAPA for SAP HANA page for more details on the Zementis and SAP real-time solution for predictive analytics.