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.

Zementis and SAP HANA: Real-time Scoring for Big Data

The Zementis partnership with SAP is manifesting itself in a number of ways. Two weeks ago we were part of the SAP Big Data Bus parked outside Wells Fargo in San Francisco. This week, we would like to share with you three new developments.

1) ADAPA is not being offered at the SAP HANA Marketplace.

2) An interview with our CEO, Mike Zeller, was just featured by SAP on the SAP Blogs.


3) Zementis was again part of the SAP Big Data Bus and the "Big Data Theatre". This time, the bus was parked outside US Bank in Englewood, Colorado. We were engaged in a myriad of conversations with the many people that came through the bus about how ADAPA and SAP HANA work together to bring predictive analytics and real-time scoring to transactional data and millions of accounts, in any industry.

Visit the Zementis ADAPA for SAP HANA page for more details on the Zementis and SAP real-time solution for predictive analytics.

Real-time scoring of transactional data with ADAPA for SAP HANA

At the recent DEMO Enterprise 2014 conference, Zementis announced its participation in the SAP® Startup Focus program and launched ADAPA for SAP HANA, a standards-based predictive analytics scoring engine. 

ADAPA for SAP HANA provides a simple plug-and-play platform to deploy the most complex predictive models and execute them in real-time, even in the context of Big Data.

In joining the SAP HANA Startup Focus program, Zementis set out to address two key challenges related to the operational deployment of predictive analytics:  Agile deployment and scalable execution.

Transactional data has for years pushed the boundaries of predictive analytics. The financial industry, for example, has been using transactional data to detect fraud and abuse for decades with complex custom solutions. Real-time scoring is paramount for companies to be able to predict and prevent fraudulent activity before it actually happens.  Likewise, the Internet of Things (IoT) demands effective processing of sensor data to employ predictive maintenance for detecting issues before they turn into device failures.

To solve these challenges, Zementis combined its ADAPA predictive analytics scoring engine with SAP HANA in a true plug-and-play platform which is universally applicable across all industries.  ADAPA to serve scoring requests and execute predictive models, HANA to offload complex model preprocessing and computation of aggregates.

In this scenario, real-time execution critically depends on HANA serving complex data lookups and aggregate profile computation in a few milliseconds.  In a high-volume environment, such aggregates or lookups may have to be computed over millions of transactions.

ADAPA provides scalable real-time scoring of the core model, plus agility for model deployment through the Predictive Model Markup Language (PMML) industry standard.  Clients are able to instantly deploy existing predictive models from various data mining tools.  For example, you can take a complex predictive model from SAS Enterprise Miner, export it in PMML format and simply make it available for real-time scoring in ADAPA for SAP HANA.  The same process, of course, applies to most commercial tools, e.g. SAP Predictive Analysis, KXEN, IBM SPSS, as well as open source tools like R and KNIME.

The unique aspect of the Zementis / SAP platform is that it combines the benefits of an open standard for predictive analytics with the power of in-memory computing.

For more product details, please see http://zementis.com/saphana.htm

Zementis and Teradata Announce In-database Scoring for Big Data

As a result of its partnership with Teradata, Zementis is excited to announce the availability of the Universal PMML Plug-in (UPPI) for Teradata analytic platforms. It does not get easier than this! Simply deploy your predictive models built in R, IBM SPSS, SAS EM, ... and score your big data, directly in-database, where it resides.

The Zementis Universal PMML Plug-in (UPPI) enables the execution of standards-based predictive analytics directly within the Teradata Unified Data Architecture™. Users can now easily deploy predictive models built in R, IBM SPSS, SAS EM and other popular analytic tools on Aster and/or Teradata to achieve scale. The bridge between these systems is PMML, the Predictive Model Markup Language standard. It allows for models to be instantly moved from the scientist's desktop to the database where they will be executed.

As described by Teradata's Chris Twogood, VP for Product and Services Marketing, "by partnering with Zementis, we are able to offer high performance, enterprise-level predictive analytics scoring for the major analytics tools that support PMML. With Zementis and PMML, we are eliminating the need for customers to recode predictive analytic models in order to deploy them within our database. In turn, this enables an analyst to reduce the time to insight required in most businesses today." Available for Teradata and Teradata Aster databases, UPPI leverages the massively parallel databases as a scalable, high-performance, scoring engine that easily processes through petabyte-scale data volumes. UPPI takes full advantage of the high-performance data warehouse with its massively parallel processing capabilities for rapid execution of standards-based predictive analytics based on the PMML standard. Models built in most commercial and open source data mining tools can now instantly be deployed in Teradata or Aster. The net result is the ability to leverage the power of standards-based predictive analytics on a massive scale, right where the data resides. Read the full press release!

Predictive Models with PMML - Upcoming workshop at UCSD Extension - Oct 24-25

October 24-25, 2013
San Diego Supercomputer Center (SDSC), UC San Diego Campus

TO REGISTER, FOLLOW THE LINK BELOW:

http://extension.ucsd.edu/studyarea/index.cfm?vAction=singleCourse&vCourse=CSE-41184&vStudyAreaID=4

The Predictive Model Markup Language (PMML) is the de facto standard to represent data mining and predictive analytic models. With PMML, one can easily share a predictive solution among PMML-compliant applications and systems.

Developed in partnership with the San Diego Supercomputer Center’s (SDSC) Predictive Analytics Center of Excellence (PACE), this 2-day, hands-on workshop, will explore how the PMML language allows for models to be deployed in minutes. You will get to know its business value and the data mining tools and companies supporting PMML. You will also begin to understand the language elements and capabilities and learn how to effectively extract the most out of your PMML code.

Workshop Benefits

• Practice PMML on SDSC’s Gordon with the guidance of world class instructors from industry and academia.
• 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
• PLUS…Receive a comprehensive tour of SDSC to discover its inner workings, extensive capabilities and current projects.

Instructors

• Alex Guazzelli, Ph.D., Vice President of Analytics, Zementis, Inc.
• Natasha Balac, Ph.D., Director of PACE, SDSC, UC San Diego
• Paul Rodriguez, Ph.D., Research Programmer Analyst, SDSC, UC San Diego

Scholarships Available!
Thanks to the generous underwriting of Zementis, three (3) half-tuition scholarships are available. Learn more and apply

Note: Students should have a fundamental knowledge of data mining methods and basic experience with computer programming language. Students must bring a laptop (MAC or PC) each day to fully participate during the hands-on portion of the workshop.

Course Number: CSE-41184   Credit: 2 units

This course is part of the following Certificate Program(s):

• Data Mining

TO REGISTER, FOLLOW THE LINK BELOW:

http://extension.ucsd.edu/studyarea/index.cfm?vAction=singleCourse&vCourse=CSE-41184&vStudyAreaID=4

PMML Workshop at KDD 2013 and UCSD Extension PMML Class

KDD 2013 PMML Workshop

Join us for the KDD PMML Workshop to be held in Chicago on August 11. Organized by the Data Mining Group (DMG), this workshop will feature invited talks and presentations of selected papers.

Zementis will be presenting two papers about PMML-support in R: Coding and representing data transformations and model through the pmmltransformations and pmml packages.


UCSD PMML Class (Coming this Fall)

UCSD Extension has teamed up with the San Diego Supercomputer Center Predictive Analytics Center of Excellence (PACE) and Zementis to offer a PMML class to the data mining community on October 24 and 25.

For more information about this great opportunity to learn the standard that is revolutionizing how predictive solutions are documented and deployed, refer to the UCSD Extension catalog.

The Zementis Partnership with FICO

Stuart Wells, FICO CTO, announced the strategic partnership between Zementis and FICO at FICO World on May 2, 2013. FICO clients will now benefit from the outstanding Zementis scoring technology.

How? The Zementis ADAPA scoring engine provides a highly scalable framework to deploy, integrate, and execute complex data mining and predictive models based on the PMML standard. Models built in most commercial and open source data mining tools, such as FICO Model Builder or R, can now instantly be deployed in the FICO Anaytic Cloud. 

Customers, application developers and FICO partners will be able to extract value and insight from their predictive models and data immediately, using ADAPA and PMML. This will result in quicker time to innovation and value on their analytic applications.

Read the press release!

Predictive Analytics Deployment

Zementis offers software solutions that enable scalable, real-time execution of predictive analytics across a variety of platforms based on the PMML standard. These include:

ADAPA Scoring Engine: Our solution for real-time scoring. ADAPA is available for on-site deployment as a traditional license or as a service in the Amazon Elastic Compute Cloud (EC2) and IBM SmartCloud Enterprise. And now, with our FICO partnership, ADAPA will also be available in the FICO Analytic Cloud.

UPPI, the Universal PMML Plug-in: The leading solution for Big Data, UPPI provides scoring in-database and for Hadoop. It is available for EMC Greenplum, IBM Netezza, SAP Sybase IQ, Teradata/Aster as well as Hadoop/Hive and Datameer. 

The Zementis Partnership with Infocom in Japan

It is our pleasure to announce a strategic partnership with Infocom. If you missed out on our press release, here is the headline:

Zementis and Infocom partner to deliver predictive analytic solutions in Japan.



Dedicated to the Japanese market, Infocom combines strong expertise in data mining and predictive analytics with extensive delivery and consulting capabilities.

Zementis offers software solutions that enable scalable, real-time execution of predictive analytics across a variety of platforms based on the PMML standard. These include the ADAPA Scoring Engine available for on-site deployment or in the cloud, and UPPI, the Universal PMML Plug-in for in-database scoring and Hadoop (available for IBM Netezza, Teradata/Aster, EMC Greenplum, SAP Sybase IQ as well as Hadoop and Datameer).

Infocom will market, distribute and support Zementis's predictive analytics software in Japan.

To take a look at the press release, click HERE.

Additional Online Resources

• Visit the Zementis resources pages for videos and articles on our products and PMML
• Follow @Zementis on Twitter
• Join the PMML discussion forum on LinkedIn

Predictive Model Markup Language (PMML) Workshop at KDD 2013 in Chicago

Please join us for a Predictive Model Markup Language (PMML) workshop at KDD 2013 in Chicago on August 11, 2013, to exchange exciting new developments, leading practices, and high impact applications in big data, knowledge discovery and data mining which utilize the PMML standard. 

The annual ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD) is the premier international forum for data mining and big data researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. We invite submission of papers describing implementations of the Predictive Model Markup Language (PMML). Submitted papers will go through a competitive peer review process. Please consult the workshop website for full details regarding paper preparation and submission guidelines. 

PMML workshop website 
http://kdd13pmml.wordpress.com/ 

KDD conference web site 
http://www.kdd.org/kdd2013/

R PMML Support: BetteR than EveR

Once represented as a PMML file, a predictive solution (data transformations + model) can be readily moved into the operational environment where it can be put to work immediately. That's the promise of PMML.

R is living up to that promise through its strong PMML export capabilities. The latest addition to the list of supported model types is Naive Bayes classifiers. More specifically, the R PMML package allows for PMML export for Naive Bayes models built using the naiveBayes function of the e1071 package. 

For more details and for a complete list of supported model types (as well as data pre-processing), click HERE.

Making the case for PMML and ADAPA

If you are not familiar with PMML, the Predictive Model Markup Language, you may be wondering what all the fuss is about ...

PMML is the de facto standard to represent data mining and predictive analytic solutions. With PMML, one can easily share a predictive solution among PMML-compliant applications and systems  For example, you can build your model in R, export it in PMML, and use ADAPA, the Zementis Scoring Engine, to deploy it in production.

Many data mining models are a one-time affair. You use historical data to build the model and use it to analyze ... historical data. Wait! That sounds more like descriptive analytics, not predictive analytics. Well, that is sort of true. To be truly predictive, a data mining model needs to be applied to new data. These are the models that need to be operationally deployed and, from my point of view, these are the solutions that are truly revolutionizing the way we do business and live in the Big Data world.

If you want then to use your data mining model to make predictions when presented with new data, it needs to be a dynamic asset. It cannot be static. You need to be able to build it and instantly put it to use. And, that's where PMML and ADAPA come in handy.

Obviously, a few data mining tools try to lock you in. You happily build the model using tool A, just to realize that you need the same tool to execute it. In this case, you are missing out. Here are some of the benefits of moving your predictive model to ADAPA:

• Overcome speed/memory limitations
• Dramatically lower your infrastructure cost
• Tap into all the advantages of cloud computing with ADAPA on the Cloud (IBM SmartCloud or Amazon EC2)
• Produce scores in real-time (using Web Services or Java API), on-demand, or batch-mode
• Execute your models directly from Excel, by using the ADAPA Add-in for Excel
• Benefit from using a set of PMML-compliant model development tools (best of breed)
• Deploy your models in minutes
• Manage models via Web Services or a Web console
• Upload one or many models into ADAPA at once
• Benefit from the seamless integration of business rules and predictive models (yes, for those who need it, ADAPA comes with a business rules engine)

PMML and ADAPA allow you to use best of breed tools (not the same old tool) for the job at hand. Also, you can leverage the expertise from a diverse group of data scientists. That means, not all your data scientists need to be experts on a single tool. They can use different tools that share one thing in common, the PMML standard. And, once represented in PMML, models can be easily understood by all team members. PMML allows for transparency and, in doing so, fosters best practices.

Why not benefit from: 1) an open standard to represent data mining models; and 2) a proven scoring engine that consumes any version of PMML and make it available for execution right away, in real-time?

Keep also in mind that ADAPA's sister product, the Universal PMML Plug-in (UPPI), allows you to move the same PMML file in-database or Hadoop. UPPI is currently available for EMC Greenplum, SAP Sybase IQ, IBM Netezza, and Teradata/Aster. With UPPI for in-database scoring, there is no need to move your data outside the database. Data and models reside inside it and so there is minimal data movement and maximum scoring speed. UPPI is also available for Datameer and will soon be available for Hadoop/Hive.

Making a model operational in minutes has never been easier! And, it is all because of PMML and scoring tools such as ADAPA and UPPI.

The Zementis Partnership with Teradata

The partnership between Zementis and Teradata allows customers with a variety of data mining tools to efficiently deploy predictive models based on the Predictive Model Markup Language (PMML) standard.  Focused on Big Data applications, the Universal PMML Plug-in (UPPI) for Teradata enables scalable execution of standards-based predictive analytics directly within the Teradata data warehouse.

To read more about the benefits of running your predictive solutions inside Teradata and Teradata Aster, please visit:

http://www.teradata.com/templates/Partners/PartnerProfile.aspx?id=12884902321

PMML Scoring

Zementis offers a range of products that make possible the deployment of predictive solutions and data mining models built in all the top commercial and open-source data mining vendors. Our products include the ADAPA Scoring Engine for real-time scoring and UPPI, which is currently available for a host of database platforms as well as Hadoop/Datameer. For a list of available platforms, please visit our in-database products page.

Rationale

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 this case 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

R PMML Support: Data Transformations

R and PMML Export     R is becoming the tool of choice for many data scientists. It is no wonder that many commercial and open-source statistical tools are also embracing R. Predictive Models A set of robust predictive analytic techniques is but one set of tools available to data scientists in R. Another important set is the ability to export PMML for a host of predictive models.  By using the pmml package (version 1.2.33 or higher), users can export PMML from R for: Random Forest Models Neural Networks Clustering Models Cox Regression Models Linear and Logistic Regression Models Support Vector Machines Association Rules Generalized Linear Models Random Survival Forest Models Data Transformations And now, another R package extends this functionality by providing PMML export for data transformations. The new pmmlTransformations package has just made its way to CRAN (the Comprehensive R Archive Network).  Want to apply a Z-scoring normalization procedure to your continuous input variables before presenting them to a neural network? No problem. Use the pmmlTransformations package in conjunction with the pmml package (version 1.2.33 or higher) to export the entire process (pre-processing + model) into a PMML file.  To look at the package's documentation in CRAN, click HERE.

Agile Predictive Analytics Deployment Once represented as a PMML file, a predictive solution (data transformations + model) can be readily moved into the operational environment where it can be put to work immediately. That's the promise of PMML. Zementis offers a host of products for the agile deployment and execution of your PMML-based solutions. Our ADAPA and UPPI scoring engines are available for: Hadoop: Datameer and Hadoop/Hive In-database: EMC Greenplum, IBM Netezza, SAP Sybase IQ, Teradata, and Teradata Aster Cloud: Amazon EC2 and IBM SmartCloud Enterprise On-site: On your own servers Real-time or Big Data requirements? Zementis has you covered. Contact us today for more information or to schedule a presentation/demo.

Predictive Maintenance Solutions made possible by Big Data, Open Standards, and Analytics

Predictive analytics is an integral part of our daily lives. At this very moment, predictive solutions are busy at work, monitoring financial transactions for fraud and abuse, recommending movies and other products, or selecting the next best offer you will get from your favorite store. As much as it permeates our lives today, the application of predictive analytics is bound to increase. For example, boosted by Big Data and cost efficient processing in the cloud, predictive maintenance applications are on their way towards becoming ubiquitous.

Predictive maintenance solutions are based on the idea that one is able to know that a machine or equipment is going to fail, and take proactive actions to ensure process reliability and safety. By using data from sensors that capture vibration information from rotating equipment, my team built a predictive maintenance solution that alerted personnel of eminent breakdowns. For that, we used a combination of statistical tools. For example, we used R, an open-source statistical package for data analysis, IBM SPSS Statistics for analysis and model building, and the Zementis ADAPA platform for model deployment. Since all these systems support PMML, the Predictive Model Markup Language, instead of spending time translating code from one system to another, we were able to concentrate on the problem itself and use the tools we trusted the most to get the job done.


PMML is the de facto standard used to represent predictive analytics or data mining models. With PMML, a predictive solution may be built in one system and deployed in another where it can be put to work immediately. The adoption of PMML by all the major analytic vendors is a testimony to their commitment to interoperability and the advancement of predictive analytics as a critical factor to the betterment of society. PMML is developed by the Data Mining Group (DMG), a committee composed not only by commercial and open-source analytic companies including IBM, SAS, Zementis, FICO, Salford Systems, Microstrategy, Togaware, KNIME and Rapid-I, but also by analytic users such as NASA, Visa, the San Diego Supercomputer Center, and Equifax.

Predictive analytics and open standards can provide yet another tool for safe guarding operations and ensuring safety and process reliability. While predictive analytics can offer solutions to alert us of problems before they actually happen, open standards such as PMML are key ingredients for ensuring that the building and deployment of predictive maintenance solutions is application independent and so agile and transparent.

We recently wrote a series of two articles for the IBM developerWorks website that covers PMML and predictive maintenance. To read both articles in their entirety, please refer to the following links:


1)What is PMML? Explore the power of predictive analytics and open standards

2)Representing predictive solutions in PMML: Move from raw data to predictions

Predictive model deployment and execution made easy with PMML

Developed by the Data Mining Group (DMG), an independent, vendor led committee, PMML provides an open standard for representing data mining models. In this way, models can easily be shared between different applications avoiding proprietary issues and incompatibilities. Currently, all major commercial and open source data mining tools support PMML. These include IBM/SPSS, SAS, KXEN, TIBCO, STATISTICA, Microstrategy, R, KNIME, and RapidMiner (for a list of PMML-compliant tools, see of PMML-powered tools at DMG.org).

PMML is an XML-based language which follows a very intuitive structure to describe data pre- and post-processing as well as predictive algorithms. Not only does PMML represent a wide range of statistical techniques, but it can also be used to represent input data as well as the data transformations necessary to transform raw data into meaningful features.

PMML Conversion

Given that a tool may generate an older version of PMML (earlier than its latests), Zementis has worked out a way to convert older versions of PMML to its latest, version 4.1. This conversion proces is also used to validate a data mining model against the PMML specification for versions 2.0, 2.1, 3.0, 3.1, 3.2, 4.0 and 4.1. If validation is not successful, the conversion process gives back a file containing explanations for why the validation failed as comments embedded in the PMML file.

Before actual conversion takes place, the validation phase needs to be successful, i.e. the model file needs to conform to the PMML specification as published by the DMG (for any of the older PMML versions listed above). For known PMML issues (from a variety of sources/vendors), the conversion process will actually correct the model file so that it can be converted appropriately.

The ADAPA Decision Engine

If you are using the ADAPA Decision Engine (or any of our scoring products), the conversion process described above is automatically executed every time a PMML file is uploaded. By doing that, ADAPA understands PMML files generated by different vendors in all the different PMML versions. Besides syntactic validation, ADAPA also validates PMML from a semantic perspective.

And so, once a model is successfully uploaded in ADAPA, it is syntactically and semantically sound. For more details, click HERE.

You can benefit from ADAPA today by signing up for your private ADAPA instance on the Amazon Cloud or on the IBM SmartCloud. You can also sign up for the ADAPA free trial.

Start executing your models right now!

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: 

http://www.youtube.com/watch?v=r_g99-kP_BE

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.

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.

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.

Predicting the future ... in four parts

I recently finished writing a four-part article series about predictive analytics entitled Predicting the Future. The topic is near and dear to my heart, since I have been working on the field since my undergrad years back in Brazil (more than 20 years ago). And, lately, through my work with PMML, the Predictive Model Markup Language.

The four articles have just been published by IBM in their entirety in the developerWorks website together with a video in which I introduce each article.



The article themselves can be found here:

1. Predicting the future, Part 1: What is predictive analytics?
2. Predicting the future, Part 2: Predictive modeling techniques
3. Predicting the future, Part 3: Create a predictive solution
4. Predicting the future, Part 4: Put a predictive solution to work

And, if you are interested in learning about open-standards and predictive analytics, I would also recommend the following articles:

• Predictive Analytics in Healthcare: The importance of open standards
• What is PMML? Explore the Power of Predictive Analytics and Open Standards
• Representing predictive solutions in PMML: Move from raw data to predictions

Enjoy!

Webcast: Predictive Analytics on Hadoop

UPDATE: Thanks for your interest in our joint webinar with Datameer: Predictive Analytics on Hadoop. If you were not able to attend or would like to watch it again at your own pace, just click HERE.


To extract value and insight from "Big Data", leading organizations increasingly leverage predictive analytics. By using statistical techniques that uncover important patterns present in historical data, companies are able to predict the future. In doing so, they become more precise, consistent and automated in everyday business decisions.

Please join the Datameer/Zementis webcast entitled Predictive Analytics on Hadoop: Gaining Faster Insights through Open Standards to learn to efficiently derive predictions from very large volumes of structured and unstructured data.

WHEN: Thursday, July 19, 2012, 10:00 am PT / 1:00 pm ET

Free registration 

In this webinar, we showcase the technical capabilities of the Universal PMML Plug-in for Datameer, a solution that combines open standards and Hadoop to reduce complexity and accelerate time-to-market for predictive analytics in any industry and for any business application.

Leave this webinar knowing:

• The benefits of the Predictive Model Markup Language (PMML) standard as a data science best practice for data mining 
• How to leverage predictive analytics in the context of big data 
• How to reduce the cost and complexity of predictive analytics 

 You can register HERE