Wednesday, October 1, 2014

Zementis/Teradata Whitepaper: Massively Parallel In-database Predictions with PMML


Zementis and Teradata have teamed up to make available to you a whitepaper which not only discusses the benefits of in-database scoring using UPPI for Teradata/Aster but also shares performance numbers that will blow you away! Enjoy!

DOWNLOAD WHITEPAPER

Abstract

Open standards enable interoperability and portability across systems and solutions. Such a level of flexibility creates new opportunities for addressing exceedingly demanding business 
agility and performance requirements. The Predictive Model Markup Language (PMML) is the embodiment of an open standard and delivers such benefits in the world of data mining and predictive analytics. This means that models developed in any environment and tool set can be deployed and used in a completely different system. 

In the context of Big Data, the urgent need to apply the power of predictive analytics to derive reliable predictions-and, hence, business decisions-from vast amounts of data collected by 
many organizations is a key requirement. In this paper, we discuss how the PMML standard enables embedding advanced predictive models directly into the database or the data warehouse, alongside the actual data to be scored. More importantly, we show how we can easily take advantage of a highly parallel database architecture to efficiently derive predictions from very large volumes of data.

DOWNLOAD WHITEPAPER

Monday, August 25, 2014

Hortonworks/Zementis Webinar: Hadoop’s Advantages for Machine Learning and Predictive Analytics

Please join Ofer Mendelevitch, Director of Data Science of Hortonworks and Michael Zeller, Founder and CEO of Zementis as they present key learnings as to what drives successful implementations of big data analytics projects. Their knowledge comes from working with dozens of companies from small cloud-based start-ups to some of the largest companies in the world.

When: Wednesday, September 10, 2014 at 10 am PST / 1 pm EST

REGISTRATION

Hortonworks will present their approach to using Apache Hadoop for predictive models with big data, and the benefits of Hadoop to data scientists. Zementis will demonstrate how to quickly deploy, execute, and optimize predictive models from open source machine learning tools like R and Python as well as commercial data mining vendors like IBM, SAP and SAS.

Zementis leverages the PMML open industry standard (Predictive Model Markup Language) providing a higher ROI for Big Data and predictive analytics initiatives. At the same time reducing IT costs, and improving the quality of predictive model management while requiring no change in how data science teams do their day-to-day work.

Whether your company is just beginning to work with predictive analytics or has an experienced data science team this webinar will provide valuable insights on how to move predictive models into an operational environment based on Hadoop and Hive and using open industry standards while eliminating the custom coding and delays typically associated with these projects. Please join us for this exciting presentation and discussion.

REGISTRATION

Wednesday, August 20, 2014

Zementis Sponsors SIGKDD 2014 Test of Time Award

The SIGKDD Test of Time Award recognizes outstanding papers from past KDD conferences beyond the last decade that have had an important impact on the data mining research community.  SIGKDD is the ACM Special Interest Group for Knowledge Discovery and Data Mining.  Not only since the advent of “Big Data”, but for 20 years, the annual SIGKDD conference has been the leading global forum for data scientists and practitioners from academia, industry and government to disseminate cutting edge research results and to demonstrate innovative applications.


It is our privilege to support the SIGKDD 2014 Test of Time Award as it recognizes influential contributions published in KDD conference proceedings which have had a substantial impact on data science.  Selected by a committee of leading scientists and supported by thousands of citations since their original publication, one could almost call it the “Nobel Prize in Data Science.”





The following three papers were selected by the award committee to receive the inaugural award:

  • A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise [KDD 1996]
  • Integrating Classification and Association Rule Mining [KDD 1998]
  • Maximizing the Spread of Influence through a Social Network [KDD 2003]
For abstracts and additional details, please see the SIGKDD web site blog.

Please join us at KDD 2014 in New York City, August 24-27, to celebrate the winners at an interdisciplinary event which will bring together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

Monday, August 18, 2014

UCSD's John Freeman interview with Alex Guazzelli, Zementis CTO: Predictive Analytics, Big Data, and PMML


Dr. Alex Guazzelli, Zementis CTO, has been extra busy lately by teaching a class at UCSD Extension entitled "Predictive Models with PMML". As the 6-week course is nearing its end for this Summer quarter, Dr. Guazzelli was invited by John Freeman, Director of Communications for UCSD Extension, for an interview  on UCTV's Career Talk to discuss Predictive Analytics, Big Data and PMML.

The interview itself was broadcast last week and it is now AVAILABLE ONLINE.

Tuesday, July 22, 2014

Alpine Data Labs/Zementis Webinar - Breaking Down Barriers for Predictive Analytics

If you are interested in learning about how PMML, the Predictive Model Markup Language standard, is being used by Alpine Data Labs and Zementis to instantly move predictive analytic models from the scientist's desktop into the IT operational environment, be sure to join us for the upcoming Alpine Data Labs/Zementis webinar on July 30th, featuring Steven Hillion, CPO of Alpine Data Labs, and Michael Zeller, CEO of Zementis.

Register here!



This will be an engaging, fast-paced and informative presentation and discussion of the latest tools and trends in predictive analytics. The webinar will include a demo of the PMML capabilities in Alpine Data Labs Chorus 4.0 and instant deployment of predictive models via Zementis solutions.

Title:

Breaking Down Barriers for Predictive Analytics


When?

Wednesday, July 30 2014 at 1 pm ET / noon CT / 11 am MT / 10 am PT / 5 pm GMT

Register here!

Thursday, July 17, 2014

ADAPA with PMML 4.2 support now available on the AWS Marketplace

Zementis has been offering its ADAPA scoring engine as a service on the Amazon Cloud for a few years now. With ADAPA on the Amazon Cloud, companies all over the world benefit from fast deployment and execution of predictive analytics via Web-services and PMML, the Predictive Model Markup Language. You can even launch your own ADAPA instance in the cloud through the AWS Marketplace with a single click.



ADAPA and its sister product, the Universal PMML Plug-in (UPPI) are PMML-based scoring engines. That is, they can consume predictive models built in any data mining tool as long as the model is represented in PMML, the Predictive Model Markup Language standard. PMML is supported by most commercial and open-source data mining tools, including FICO, IBM SPSS, KNIME, RapidMiner, R, SAS, and SAP. With PMML, one can simply move a predictive model from the scientist's desktop where it was built to the IT operational environment with no need for custom code.

Zementis was the first company to announce compatibility with PMML 4.2, the latest version of the PMML standard. PMML 4.2 introduces extensive text mining capabilities into the standard and now Zementis is bringing these exciting new PMML features to its AWS customers. Learn about all the new cool features introduced in PMML 4.2.

It is really super simple to deploy and score your models using ADAPA. And now, with PMML 4.2 support on the Amazon Cloud, predictive analytics as a service has just become amazingly powerful.

Visit the Zementis website for details

Monday, June 30, 2014

Zementis presents at useR! 2014 - Happening now at UCLA


useR! 2014 is happening now at UCLA. For more information, see: http://user2014.stat.ucla.edu/

The useR! conference is the main gathering of R users and experts in the planet. It features invited talks, tutorials, presentations and posters. This year, Zementis is giving a presentation on Model Ensembles and PMML. It will take place on Tuesday (July 1st) at 4 PM PST.


For the abstract of our presentation, please refer to: http://user2014.stat.ucla.edu/abstracts/talks/112_Jena.pdf

Zementis will also be presenting a poster on Tuesday at 5:30 PM PST. This poster will showcase the pmmlTransformations package. For the abstract of our poster presentation, please refer to: http://user2014.stat.ucla.edu/abstracts/posters/113_Jena.pdf

PMML, the Predictive Model Markup Language, is the perfect vehicle for the deployment of predictive analytics. It is imperative for the deployment of model ensembles such as Random Forest Models, which are usually composed by hundreds if not thousands of decision trees. PMML is supported in R via the pmml and pmmlTransformations packages. For a detail description of these packages, please refer to:
https://support.zementis.com/entries/21197842-PMML-Export-Functionality-in-R-Supported-Packages

Thursday, June 19, 2014

Introducing Py2PMML (Python to PMML)

The Zementis Python to PMML Converter (Py2PMML) provides you with an easy to use interface to translate your Python-generated machine learning models into PMML, the Predictive Model Markup Language standard. In particular, it allows for models built using scikit-learn to be consumed by Zementis ADAPA and UPPI scoring engines.

Once translated into PMML, models can be easily deployed and scored against new incoming data. For example, models can be deployed in ADAPA for real-time scoring or UPPI for big data scoring in-database or Hadoop.

How does it work?


Easy! Once you build your model using the scikit-learn library, all you need to do is write out a .txt file containing the model's parameters. The .txt file needs to follow a strict order and contain all the required information. This is the file used by Py2PMML to generate the corresponding PMML file for your model. With the PMML file in hand, you can simply deploy it in ADAPA for real-time scoring or UPPI for big data scoring.



What are the supported model types?


As of now, the supported scikit-learn predictive modeling classes are:

Supported pre-processing classes are (contact us for details):

  • Class MinMaxScalerStandardizes features by scaling each feature to a given range
  • Class OneHotEnconder - Creates dummy continuous variables out of categorical variables
  • Missing Value Replacement
To learn exactly how each .txt file needs to be generated so that Py2PMML can do its job, please take a look at the specific posting for the particular model type you are interested in converting to PMML.

References


Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.

Monday, June 9, 2014

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.


Thursday, May 29, 2014

Zementis is a finalist for the SAP 2014 Startup Focus Award

Zementis is proud to be a finalist for the SAP 2014 Startup Focus Award for the Most Innovative company category. The list of all finalists has just been announced.

http://www.saphana.com/community/learn/startups/news-views/blog/2014/05/28/2014-startup-focus-award-finalists

Customers are increasingly facing the challenge of implementing more intelligent real-time decisions within the context of big data. Business insights are critical for making intelligent business decisions, and these insights often lie buried in massive volumes of fast-changing and increasingly varied data. Predictive analytics based on statistical algorithms and machine learning can reveal these insights.

Once an organization’s data science team has developed predictive models, the team must then collaborate with the internal IT organization to deploy those models so that business users can incorporate predictive analytics into their decision making. For a data-driven enterprise, the agile deployment, integration and execution of predictive models has become an essential strategic capability.
Zementis and SAP have partnered to deliver this capability to enterprises and enable consistent, accurate predictive analytics as an operational capability, at scale. Our joint solution combines Zementis ADAPA, a scoring engine for predictive analytics, with SAP HANA, the premier platform for in-memory computing.

As a joint solution, ADAPA for SAP HANA represents a universal platform for the operational deployment and execution of predictive analytics. It delivers:
  • Real-time scoring through HANA
  • Superior performance via super-fast in-memory processing
  • High scalability, to support dynamic computing requirements associated with real-time big data
  • Inherent flexibility to support complex computations irrespective of predictive model type or data volume

With ADAPA for SAP HANA, organizations become agile consumers of big data for predictive analytics. Zementis and SAP have removed the complexity of this critical business capability, freeing organizations to focus on developing the best possible predictive models and using those models to make the most intelligent business decisions.

For more details about ADAPA for SAP HANA please contact Zementis, or download the ADAPA for SAP HANA data sheet and watch a demo video. It shows how Zementis and SAP have overcome the challenge of fraud detection in e-commerce through the use of predictive analytics.





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