Friday, August 31, 2012

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:
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.

Thursday, August 9, 2012

Agile Deployment of Predictive Analytics on Hadoop: Faster Insights through Open Standards

This joint Datameer/Zementis presentation given at the 2012 Hadoop Summit outlines the benefits of the PMML standard as key element of data science best practices and its application in the context of distributed processing. In a live demonstration, we showcase how Datameer and the Zementis Universal PMML Plug-in (UPPI) take advantage of a highly parallel Hadoop architecture to efficiently derive predictions from very large volumes of data.

Watch it now on YouTube:

Session Abstract:

While Hadoop provides an excellent platform for data aggregation and general analytics, it also can provide the right platform for advanced predictive analytics against vast amounts of data, preferably with low latency and in real-time. This drives the business need for comprehensive solutions that combine the aspects of big data with an agile integration of data mining models. Facilitating this convergence is the Predictive Model Markup Language (PMML), a vendor-independent standard to represent and exchange data mining models that is supported by all major data mining vendors and open source tools (see figure below).

PMML is an XML-based language developed by the Data Mining Group (DMG) which provides a way for applications to define statistical and data mining models and to share models between PMML compliant applications. It provides applications a vendor-independent method of defining models so that proprietary issues and incompatibilities are no longer a barrier to the exchange of models between applications. PMML allows users to develop models within one vendor's application, and use another vendors' applications to visualize, analyze, evaluate or otherwise use the models. Previously, this was very difficult, but with PMML, the exchange of models between compliant applications is now straightforward.

Wednesday, August 1, 2012

TOP 10 PMML Resources

We offer you a host of free 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 on 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) 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.

3) 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.

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

5) 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.

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

7) 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.

8) PRESENTATION: Our PMML presentation at LinkedIn earlier this year to the ACM Data Mining Bay Area/SF group is available for on-demand viewing on YouTube. Presentation slides can be donwloaded HERE.

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

10) 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.

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