Thursday, October 4, 2012

TOP 10 PMML Resources

These are the top 10 on-line resources that allow you to expand your PMML skills. With these, you can learn how to best operationalize your predictive models, not only on your own infrastructure, but also on the cloud, in-database, or Hadoop.

Your peers are already communicating predictive analytics with PMML. Learn how you too can benefit from it.

1) BOOK: We have recently published the 2nd edition of our PMML book. Entitled "PMML in Action", the book is available on in paperback or in kindle format.

2) TALK/PRESENTATION: Our PMML talk at LinkedIn earlier this year to the ACM Data Mining Bay Area/SF group is available for on-demand viewing on YouTube. Slides are also available and cab be downloaded HERE.

3) BLOGS: Another great resource for PMML related material is the blog site. Besides highlighting the standard itself, this site also discusses the latest PMML support offered by producers and consumers.

4) VIDEOS: We have been busy producing informative webinars with our partners. You can find all our past webinars (including joint webinars with IBM SPSS and Revolution) by visiting our videos page.

5) ARTICLES: White-papers (including joint papers with KNIME and EMC), peer-reviewed articles and invited articles. Check them out! Visit the Zementis articles page.

6) TOOLS: Our tools page contains the description and link to the Transformations Generator, which allows you to graphically design your transformations and export them into PMML.

7) FORUMS: A place to ask questions and discuss model deployment. Explore and join our community forums.

8) EXAMPLES: In the DMG PMML Examples page, you not only can find typical predictive models such as neural networks and decision trees, but also association rules and random forest models.

9) NEWSLETTER: The latest information on PMML and model deployment. Our Deploy! Newsletter is now on its 21st issue.

10) DISCUSSION GROUP: Last, but not least, you are welcome to join the PMML discussion group in LinkedIn now with close to 3,000 members and growing fast.

Tuesday, October 2, 2012

Big Data Made Easy with In-database Scoring

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 on an accelerated pace.

A new day has come!

Zementis is now offering its amazing Universal PMML Plug-in™ (UPPI) for in-database scoring for the IBM Netezza appliance, SAP Sybase IQEMC Greenplum, Teradata and Teradata Aster.

Amazing! Why?

For starters, it won't break your budget (feel free to contact us for details). Also, it is simple to deploy and maintain. Our Universal PMML Plug-in was designed from the ground up to take advantage of efficient in-database execution. Last but not least, as its name suggests, it is PMML-based. PMML, the Predictive Model Markup Language is the standard for representing predictive models currently exported from all major commercial and open-source data mining tools. So, if you build your models in either SAS, IBM SPSS, STATISTICA, or R, you are ready to start benefiting from in-database scoring right away.

The PMML plugin seamlessly embeds models within your database. Data scoring requires nothing more than adding a simple function call into your SQL statements. You can score data against one model or against multiple models at the same time. There is no need to code complex data transformations and calculations in SQL or stored procedures. PMML and our Universal Plug-in can easily take care of that.

Modeling techniques currently supported are:

  • Neural Networks
  • Support Vector Machines
  • Naive Bayes Classifiers
  • Ruleset Models
  • Clustering Models (including Two-Step Clustering)
  • Decision Trees
  • Regression Models (including Cox Regression Models)
  • Scorecards (including reason codes)
  • Association Rules
  • Multiple Models (model composition, chaining, segmentation, and ensemble - including Random Forest models)

  • As well as extensive data pre- and post-processing capabilities.

    In addition to all these predictive techniques, UPPI accepts PMML models of all versions (2.0, 2.1, 3.0, 3.1, 3.2, 4.0, 4.1 and 4.2) generated by any of the major commercial and open source mining tools (SAS, SPSS/IBM, STATISTICA, MicroStrategy, Microsoft, Oracle, KXEN, Salford Systems, TIBCO, R/Rattle, KNIME, RapidMiner, etc.). It does not get more universal than this!

    Copyright © 2009-2014 Zementis Incorporated. All rights reserved.

    Privacy - Terms Of Use - Contact Us