Wednesday, November 14, 2012

Universal PMML Scoring for Teradata and Aster

Big Data and PMML, the Predictive Model Markup Language, are hot topics these days. But, when combined with in-database scoring, they take a new and powerful meaning. It is then no wonder that Zementis is thrilled to announce its partnership with Teradata, a global leader in data warehousing and analytics.

Teradata and Zementis


Zementis is pleased to announce that its Universal PMML Scoring Engine (UPPI) will soon be available on the Teradata and Aster databases.

Zementis offers a range of products that make possible the deployment of predictive solutions and data mining models built in all the top commercial and open-source data mining vendors. Our products include the ADAPA Decisioning Engine for real-time scoring and UPPI, which is currently available for a host of database platforms as well as Hadoop/Datameer.


With UPPI for Teradata and UPPI for Aster, Zementis is expanding considerably the number of advanced platforms able to combine in-database scoring and data warehousing for rapid, on-the-fly predictive analytics on large volumes of data. 

UPPI for Teradata and UPPI for Aster enable analytic enterprises to realize significant business value from new business models and help companies drive both top-line revenue growth and bottom-line cost savings.
  
Check out the Zementis website for webinars, presentations and product data sheets and to learn more about in-database scoring with UPPI.

Big Data, Predictive Analytics and PMML


Not all analytic tasks are born the same. If one is confronted with massive volumes of data that need to be scored on a regular basis, in-database scoring sounds like the logical thing to do. In all likelihood, the data in this case is already stored in a database and, with in-database scoring, there is no data movement. Data and models reside together hence scores and predictions flow on an accelerated pace. 

Need some context? Dr. Alex Guazzelli, our VP of Analytics, has been spreading the word about how Predictive Analytics and PMML can tackle the Big Data challenge head-on. 

Watch: 
Dr. Guazzelli's talk at Intellifest 2012 in San Diego, CA. The conference theme this year was "Intelligence in the Cloud", exploring the use of applied AI in cloud computing, mobile apps, Big Data, and many other application areas.

Watch:
Learn how PMML has turned the task of operationalizing predictive solutions into a no-brainer. Watch Dr. Guazzelli's presentation about PMML to the ACM Data Mining Group at the LinkedIn Auditorium in Sunnyvale, CA. 

Join:
Now with over 3K members (and growing quickly). 

Friday, November 9, 2012

Model Deployment with PMML, the Predictive Model Markup Language

The idea behind this demo is to show you how easy it is to operationally deploy a predictive solution once it is represented in PMML, the Predictive Model Markup Language.

As a model building environment, I use KNIME to generate a neural network model for predicting customer churn. Once data pre-processing and model are represented in PMML, I go on to deploy it in the Amazon Cloud using the ADAPA Scoring Engine and on top of Hadoop using the Universal PMML Plug-in (UPPI) for Datameer. So, the very same model is readily available for execution in two very distinct Big Data platforms: cloud and Hadoop.



The easy of model deployment and interoperability between platforms is the power of PMML, the de facto standard for predictive analytics and data mining models.

Resources:

  1. Download the KNIME workflow used to generate a sample neural network for predicting churn
  2. Download the PMML file created during the demo

Tuesday, November 6, 2012

When Big Data and Predictive Analytics Collide

Big Data is usually defined in terms of Volume, Variety and Velocity (the so called 3 Vs). Volume implies breadth and depth, while variety is simply the nature of the beast: on-line transactions, tweets, text, video, sound, ... Velocity, on the other hand, implies that data is being produced amazingly fast (according to IBM, 90% of the data that exists today was generated in the last 2 years), but that it also gets old pretty fast. In fact, a few data varieties tend to age quicker than others.

To be able to tackle Big Data, systems and platforms need to be robust, scalable, and agile.

It is in this context that IntelliFest 2012 came to be. The conference theme this year was "Intelligence in the Cloud", exploring the use of applied AI in cloud computing, mobile apps, Big Data, and many other application areas. Among several amazing speakers at Intellifest were Stephen Grossberg from Boston University, Rajat Monga from Google, Carlos Serrano-Morales from Sparkling Logic, Paul Vincent from TIBCO, and Alex Guazzelli from Zementis.

Dr. Alex Guazzelli's talk on Big Data, Predictive Analytics, and PMML is now available for on-demand viewing on YouTube. The abstract follows below, together with several resources including the presentation slides and files used in the live demo.



Abstract:

Predictive analytics has been used for many years to learn patterns from historical data to literally predict the future. Well known techniques include neural networks, decision trees, and regression models. Although these techniques have been applied to a myriad of problems, the advent of big data, cost-efficient processing power, and open standards have propelled predictive analytics to new heights.

Big data involves large amounts of structured and unstructured data that are captured from people (e.g., on-line transactions, tweets, ... ) as well as sensors (e.g., GPS signals in mobile devices). With big data, companies can now start to assemble a 360 degree view of their customers and processes. Luckily, powerful and cost-efficient computing platforms such as the cloud and Hadoop are here to address the processing requirements imposed by the combination of big data and predictive analytics.

But, creating predictive solutions is just part of the equation. Once built, they need to be transitioned to the operational environment where they are actually put to use. In the agile world we live today, the Predictive Model Markup Language (PMML) delivers the necessary representational power for solutions to be quickly and easily exchanged between systems, allowing for predictions to move at the speed of business.

This talk will give an overview of the colliding worlds of big data and predictive analytics. It will do that by delving into the technologies and tools available in the market today that allow us to truly benefit from the barrage of data we are gathering at an ever-increasing pace.

Resources:

  1. Download the presentation slides
  2. Download the KNIME workflow used to generate a sample neural network for predicting churn
  3. Download the PMML file created during the demo




Friday, November 2, 2012

Predictive Solutions for Real-Time Scoring and Big Data with ADAPA and the Universal PMML Plug-in


PMML, the Predictive Model Markup Language, allows for predictive models to be easily moved into production and operationally deployed on-site, in the cloud, in-database or Hadoop. Zementis offers a range of products that make possible the deployment of predictive solutions and data mining models built in IBM SPSS, SAS, StatSoft STATISTICA, KNIME, KXEN, R, etc. Our products include the ADAPA Decisioning Engine and the Universal PMML Plug-in (UPPI). 



SOLUTIONS FOR REAL-TIME SCORING AND BIG DATA

ADAPA, the Babylonian god of wisdom, is the first PMML-based, real-time predictive decisioning engine available on the market, and the first scoring engine accessible on the Amazon Cloud and IBM SmartCloud as a service. ADAPA on the Cloud combines the benefits of Software as a Service (SaaS) with the scalability of cloud computing. ADAPA is also available as a traditional software license for deployment on site.

As even the god of wisdom knows, not all analytic tasks are born the same. If one is confronted with massive volumes of data that need to be scored on a regular basis, in-database scoring sounds like the logical thing to do. In all likelihood, the data in these cases is already stored in a database and, with in-database scoring, there is no data movement. Data and models reside together; hence, scores and predictions flow at an accelerated pace. ADAPA’s sister product, the Universal PMML Plug-in (UPPI), is the Zementis solution for Hadoop and in-database scoring. UPPI is available for the IBM Netezza appliance, SAP Sybase IQ, and EMC Greenplum. It is also available for Hadoop/Datameer. 

BROAD SUPPORT FOR PREDICTIVE ANALYTICS AND PMML

ADAPA and UPPI consume model files that conform to the PMML standard, version 2.0 through 4.1. If your model development environment exports an older version of PMML, our products will automatically convert your file into a 4.1 compliant format. 

Our products support an extensive collection of statistical and data mining algorithms. These include:
  • Neural Networks (Back-Propagation, Radial-Basis Function, and Neural-Gas) 
  • Regression Models (Linear, Polynomial, and Logistic)
  • General Regression Models (General Linear, Ordinal Multinomial, Generalized Linear, Cox) 
  • Support Vector Machines (for regression and multi-class and binary classification) 
  • Decision Trees (for classification and regression)
  • Scorecards (including support for reason codes and complex attributes) 
  • Association Rules 
  • Ruleset Models (flat Decision Trees)
  • Clustering Models (Distribution-Based, Center-Based, and 2-Step Clustering) 
  • Naive Bayes Classifiers 
  • Multiple Models (model composition, chaining, segmentation, and ensemble - including Random Forest Models)
A myriad of functions for implementing data pre- and post-processing are also supported, including:
  • Value Mapping
  • Discretization
  • Normalization
  • Scaling
  • Logical and Arithmetic Operators
  • Conditional Logic
  • Built-in Functions
  • Lookup Tables
  • Business Decisions and Thresholds
  • Custom Functions ... and much much more
Contact us today! 

Zementis, Inc.
6125 Cornerstone Court East, Suite 250
San Diego, CA  92121
T: 619 330 0780  x2000

Visit us on the web: www.zementis.com
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Or send us an e-mail at info@zementis.com







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