Wednesday, November 12, 2014

IBM and Zementis Release White Paper: "Enhancing Predictive Analytics"

Continuing their longstanding partnership, Zementis and IBM recently released a white paper that details how organizations can improve their business agility through predictive analytics. The paper describes some of the key benefits that organizations can derive from applying predictive analytics to key decisions, outlines some of the operational and technical challenges that organizations commonly face in this effort, and showcases the capabilities that IBM and Zementis make possible to unlock the full potential of big data through predictive analytics.

Together, Zementis and IBM help enterprises overcome many challenges associated with their big data efforts, simplifying and accelerating the deployment of predictive models and making possible large-scale analytics that once seemed impractical to execute.

Zementis' UPPITM solution is integrated with several of IBM's flagship big data analytics platforms, including IBM PureDataTM System for Analytics, powered by Netezza® technology, and IBM InfoSphere® BigInsightsTM software. In each case, the joint IBM/Zementis solution helps companies deploy, execute and integrate scalable, standards-based predictive analytics. UPPI extends the in-database and Hadoop-based predictive analytics capabilities of these IBM platforms through the use of Hive, a data warehouse system for Hadoop.

The white paper describes the benefits of Zementis' open standards approach to predictive analytics, as well as the technical capabilities of the joint solutions with IBM and the tangible benefits that organizations can realize by making IBM and Zementis a foundational element of their big data analytics strategy and architecture.

Highlights of the joint solution include:
  • Enables near real-time predictive model deployment through a universal, flexible approach
  • Reduces cost and complexity of deploying and utilizing predictive analytics for big data
  • Delivers standards-based execution of predictive analytics for in-database scoring
  • Accelerates time-to-market for enhancing intelligent decision making via predictive data
  • Supports highly dynamic and complex data environments with massively parallel processing
  • Extends the analytics functionality and business value of robust IBM platforms
Download the white paper

Thursday, November 6, 2014

Microsoft and Zementis Announce ADAPA for Azure

On October 28, Microsoft and Zementis unveiled the culmination of a collaborative effort that had begun many months before. Zementis' real-time predictive analytics decision engine, ADAPA®, became officially certified on Azure - Microsoft's dynamic and innovative cloud platform.


Microsoft Azure offers enterprise users a powerful collection of integrated services - compute, storage, data, networking, and applications - and is the only major cloud platform ranked by Gartner as an industry leader for both infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS).

With ADAPA on Azure, organizations can develop predictive models using most open source and commercial data mining tools and deploy machine learning models rapidly to generate predictive insights in real-time. New and existing Zementis customers can now take advantage of the "pay per use" model with Azure to reduce infrastructure costs and total cost of ownership.

Microsoft's Azure Marketplace makes it easy to find, purchase and launch ADAPA. Configuration is easy, and organizations can be up and running quickly with predictive analytics that leverage the efficiency, scalability, security and performance of Microsoft's enterprise-grade cloud environment.
 
"With a focus on rapid deployment and integration of predictive algorithms through open standards, Zementis embraces the cloud," said Garth Fort, General Manager of Enterprise Partners, Microsoft. "ADAPA allows customers to take advantage of the compute resources in Azure to support predictive analytics solutions and quickly scale capacity as computing requirements change, paying only for the resources used."

To purchase and deploy ADAPA from the Azure Marketplace today, please visit Microsoft Azure

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.





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