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 amazon.com in paperback or in kindle format.

2) BLOGS: Another great resource for PMML related material is the predictive-analytics.info 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.

LinkedIn
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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