Monday, December 17, 2012

Zementis, enabling Big Data and real-time scoring with PMML


Zementis, Inc. is a company that makes software for the operational deployment and integration of predictive analytics and data-mining solutions. Its main products are the ADAPA Decision Engine, a platform for statistics and data processing, and the Universal PMML Plug-in for Hadoop and in-database scoring.



The name Zementis, symbolizing "concrete thoughts", is derived from the German word Zement (cement, concrete) and the Latin word Mentis (thought, intellect) and relates to the company's core competence in machine learning and AI.

Road to ADAPA

Founded in 2004 with the goal of providing predictive analytics to the marketplace, Zementis is composed of two main divisions, analytics and engineering. Although it started as a company focused on building predictive models, Zementis scientists soon realized that their models needed a platform in which they could be easily deployed and managed. From this need, the ADAPA Decision Engine came to be.

ADAPA initially supported only neural networks, but it soon became a platform for the deployment of a myriad of statistical techniques as well as data processing (download the ADAPA Product Datasheet for a list of supported techniques). From its inception, ADAPA has been based on open-standards, including PMML, the Predictive Model Markup Language. As a member of the Data Mining Group (DMG), the committee defining PMML, Zementis has helped shaped the standard as it becomes the necessary vehicle for the sharing of predictive solutions between applications.

In 2008, ADAPA was launched as a service on the Amazon Elastic Compute Cloud (Amazon EC2) and is currently being used worldwide by companies and individuals who want to execute their predictive models and decision logic.

In 2012, ADAPA cloud offering was extended to the IBM SmartCloud. In this way, IBM provides companies around the world predictive decisions when and where they are needed.

Universal PMML Scoring Engine - UPPI

Building on the heritage of its ADAPA Decision Engine, Zementis launched the Universal PMML Plug-in (UPPI), a highly optimized, in-database scoring engine for predictive models, fully supporting the PMML standard. With PMML, UPPI delivers a wide range of predictive analytics for high performance scoring. It shortens time to market for predictive models and empowers users through instant deployment of predictive models. UPPI is available for the following DB platforms:
The Universal PMML Scoring Engine is also available for Datameer for scoring in Hadoop.

Zementis Locations

Zementis HQ is located in San Diego in California. It also has an office in Hong Kong for servicing clients in the Asia-Pacific region.

References

  • R. Nisbet, J. Elder, and G. Miner. Handbook of Statistical Analysis and Data Mining Applications. Academic Press, 2009.

Tuesday, December 11, 2012

Big Data and Real-time Scoring with ADAPA, the Universal PMML Scoring Engine

When first released, ADAPA (Adaptive Decision And Predictive Analytics) was purely a scoring engine, used to produce scores out of data mining models expressed in PMML (Predictive Model Markup Language) format. More recently, however, with the addition of a rules engine to its core, ADAPA is able to seamlessly combine rules and predictive models, which enables businesses to manage and design automated decisioning systems. In this way, ADAPA allows for the concretization of Enterprise Decision Management (EDM) solutions.

PMML Support and Predictive Analytics


Predictive analytics comprises a series of modeling techniques which can be used to extract relevant patterns present in large amounts of data to better predict the future.

ADAPA is able to generate scores out of a variety of predictive modeling techniques expressed in PMML. PMML provides a standard way for the expression of predictive models. In this way, proprietary issues and incompatibilities are no longer a barrier to the exchange of models between applications.

Currently, ADAPA supports the following PMML elements:
    • Multinomial Logistic
    • General Linear
    • Ordinal Multinomial
    • Simple Regression
    • Generalized linear model
    • Cox Regression Models
  • Multiple Models: Model Composition, Ensembles, Segmentation, and Chaining (including Random Forest Models).
as well as a variety of elements involved in data pre- and post-processing:
  • Text Mining
  • Regular Expressions
  • Built-in Functions (logic and arithmetic operators as well as conditional logic)
  • Normalization
  • Discretization
  • Value Mapping
  • Custom Functions
  • Targets/Scaling
  • Outputs (including business decisions and thresholds)
  • Model Verification (which in ADAPA can also take the form of a CSV file)
Once a model is uploaded in ADAPA, it can be executed in batch and real-time. ADAPA is a PMML consumer, therefore it is able to execute PMML code exported from tools such as R, IBM SPSS, SAS, KNIME, KXEN, STATISTICA, BigML, RapidMiner, etc.


ADAPA To Go


PMML Conversion


ADAPA provides its users with the ability to automatically convert older PMML models (versions 2.0, 2.1, 3.0, 3.1, 3.2, 4.0) to version 4.2. Besides schema validation, the conversion process also corrects known issues with PMML code from several sources/vendors. The aim is to successfully validate code in older versions of PMML and convert them to PMML 4.2. 

Transformations Generator


PMML provides a variety of data transformations, including value mapping, normalization, and discretization. It also offers several built-in functions as well as arithmetic and logical operators which can be combined to represent complex pre-processing steps. With the Transformations Generator tool, one can graphically design a transformation and obtain the respective PMML code. This can then be pasted into an existing PMML file and uploaded in ADAPA.

Software as a Service on the Cloud (Amazon EC2)


ADAPA predictive analytics is available through the Amazon Elastic Computing Cloud (Amazon EC2). It provides the first SaaS (Software as a Service) predictive decisioning platform. The user can upload and manage several rule sets as well as models expressed in PMML and score data in real-time through the use of web-service calls (ADAPA will automatically convert older versions of PMML to version 4.2 and correct any known issues from different vendors). ADAPA as a Service empowers people, since it allows for anyone anywhere to deploy and use state of the art data mining models.

ADAPA Add-in for Microsoft Office Excel


To make the process of executing predictive models even simpler, Zementis also offers the ADAPA add-in for Excel 2007, 2010, and 2013 (available for free). With the add-in, anyone in the enterprise is able to score data in Excel by executing models previously deployed in the Cloud.

ADAPA allows for real-time data scoring at any time a new event occurs since it can be used from inside any application via Web Service Calls. Excel is just one such application which happens to be a very well known tool (used by many). This is remarkable, since it frees users from having to deal with all the technology required for scoring their data whenever necessary. With the Excel add-in, all one has to do is to select which data records to score (or the columns and rows containing the relevant data) and pressing on the “Score” button in Excel … et voila’ … new predictions are generated automatically for all selected records.

ADAPA Flavors


ADAPA is currently being offered in three ways:
  • In the Amazon Cloud: launch your own private instances of ADAPA on Amazon EC2.
  • On Site: ADAPA is also available for deployment on site or on your private cloud. 

In-Database Scoring


Built on the heritage of the ADAPA Decision Engine, the Universal PMML Plug-in (UPPI) is a highly optimized, in-database scoring engine for predictive models, fully supporting the PMML standard. With PMML, UPPI delivers a wide range of predictive analytics for high performance scoring. It shortens time to market for predictive models and empowers users through instant deployment of predictive models.


UPPI is available for the following platforms:

Scoring for Hadoop


Zementis and Datameer have partnered to deliver first-ever standards-based execution of predictive analytics on a massive parallel scale. This joint solution combines the Zementis Universal PMML Scoring Engine for real-time execution of predictive models with the power and scale of Datameer, an end-to-end BI solution that includes data source integration, an analytics engine, visualization and dashboarding.

UPPI for Datameer brings together essential technologies, offering the best combination of open standards and scalability for the application of predictive analytics. The Plug-in fully supports the Predictive Model Markup Language (PMML), the de facto standard for data mining applications, which enables the integration of predictive models from IBM SPSS, SAS, R, and many more.

UPPI is also available for Hadoop/Hive. For more information see the UPPI for Hadoop page.

References

Resources

  • Zementis Support - Help desk and support forums providing support information for PMML, ADAPA, and the Universal PMML Plug-in (UPPI).
  • ADAPA product page - contains information about ADAPA on the Cloud, on Site, and the add-in for Excel.
  • Deploy! Newsletter - monthly newsletter containing the latest news on ADAPA and predictive analytics.
  • PMML - PMML resources page including examples.
  • PMML Tools - The Transformations Generator.
  • Videos - webinars and on-line video tutorials about model deployment, ADAPA, Excel add-in, PMML, ...
  • Data Mining Group (DMG) - describes PMML, the Predictive Modeling Markup Language, as well as gives information on all the companies currently supporting the standard.
  • Drools homepage
  • PMML 4.2 is here! - gives a short summary of the new features of the latest release of PMML.
  • PMML in Action (2nd Edition) - PMML book available on Amazon (Paperback and Kindle).
  • PMML Presentation - video of Dr. Alex Guazzelli's PMML presentation for the ACM Data Mining Group at LinkedIn.

Wednesday, November 14, 2012

Universal PMML Scoring for Teradata and Aster

Big Data and PMML, the Predictive Model Markup Language, are hot topics these days. But, when combined with in-database scoring, they take a new and powerful meaning. It is then no wonder that Zementis is thrilled to announce its partnership with Teradata, a global leader in data warehousing and analytics.

Teradata and Zementis


Zementis is pleased to announce that its Universal PMML Scoring Engine (UPPI) will soon be available on the Teradata and Aster databases.

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 Decisioning Engine for real-time scoring and UPPI, which is currently available for a host of database platforms as well as Hadoop/Datameer.


With UPPI for Teradata and UPPI for Aster, Zementis is expanding considerably the number of advanced platforms able to combine in-database scoring and data warehousing for rapid, on-the-fly predictive analytics on large volumes of data. 

UPPI for Teradata and UPPI for Aster enable analytic enterprises to realize significant business value from new business models and help companies drive both top-line revenue growth and bottom-line cost savings.
  
Check out the Zementis website for webinars, presentations and product data sheets and to learn more about in-database scoring with UPPI.

Big Data, Predictive Analytics and PMML


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. 

Need some context? Dr. Alex Guazzelli, our VP of Analytics, has been spreading the word about how Predictive Analytics and PMML can tackle the Big Data challenge head-on. 

Watch: 
Dr. Guazzelli's talk at Intellifest 2012 in San Diego, CA. The conference theme this year was "Intelligence in the Cloud", exploring the use of applied AI in cloud computing, mobile apps, Big Data, and many other application areas.

Watch:
Learn how PMML has turned the task of operationalizing predictive solutions into a no-brainer. Watch Dr. Guazzelli's presentation about PMML to the ACM Data Mining Group at the LinkedIn Auditorium in Sunnyvale, CA. 

Join:
Now with over 3K members (and growing quickly). 

Friday, November 9, 2012

Model Deployment with PMML, the Predictive Model Markup Language

The idea behind this demo is to show you how easy it is to operationally deploy a predictive solution once it is represented in PMML, the Predictive Model Markup Language.

As a model building environment, I use KNIME to generate a neural network model for predicting customer churn. Once data pre-processing and model are represented in PMML, I go on to deploy it in the Amazon Cloud using the ADAPA Scoring Engine and on top of Hadoop using the Universal PMML Plug-in (UPPI) for Datameer. So, the very same model is readily available for execution in two very distinct Big Data platforms: cloud and Hadoop.



The easy of model deployment and interoperability between platforms is the power of PMML, the de facto standard for predictive analytics and data mining models.

Resources:

  1. Download the KNIME workflow used to generate a sample neural network for predicting churn
  2. Download the PMML file created during the demo

Tuesday, November 6, 2012

When Big Data and Predictive Analytics Collide

Big Data is usually defined in terms of Volume, Variety and Velocity (the so called 3 Vs). Volume implies breadth and depth, while variety is simply the nature of the beast: on-line transactions, tweets, text, video, sound, ... Velocity, on the other hand, implies that data is being produced amazingly fast (according to IBM, 90% of the data that exists today was generated in the last 2 years), but that it also gets old pretty fast. In fact, a few data varieties tend to age quicker than others.

To be able to tackle Big Data, systems and platforms need to be robust, scalable, and agile.

It is in this context that IntelliFest 2012 came to be. The conference theme this year was "Intelligence in the Cloud", exploring the use of applied AI in cloud computing, mobile apps, Big Data, and many other application areas. Among several amazing speakers at Intellifest were Stephen Grossberg from Boston University, Rajat Monga from Google, Carlos Serrano-Morales from Sparkling Logic, Paul Vincent from TIBCO, and Alex Guazzelli from Zementis.

Dr. Alex Guazzelli's talk on Big Data, Predictive Analytics, and PMML is now available for on-demand viewing on YouTube. The abstract follows below, together with several resources including the presentation slides and files used in the live demo.



Abstract:

Predictive analytics has been used for many years to learn patterns from historical data to literally predict the future. Well known techniques include neural networks, decision trees, and regression models. Although these techniques have been applied to a myriad of problems, the advent of big data, cost-efficient processing power, and open standards have propelled predictive analytics to new heights.

Big data involves large amounts of structured and unstructured data that are captured from people (e.g., on-line transactions, tweets, ... ) as well as sensors (e.g., GPS signals in mobile devices). With big data, companies can now start to assemble a 360 degree view of their customers and processes. Luckily, powerful and cost-efficient computing platforms such as the cloud and Hadoop are here to address the processing requirements imposed by the combination of big data and predictive analytics.

But, creating predictive solutions is just part of the equation. Once built, they need to be transitioned to the operational environment where they are actually put to use. In the agile world we live today, the Predictive Model Markup Language (PMML) delivers the necessary representational power for solutions to be quickly and easily exchanged between systems, allowing for predictions to move at the speed of business.

This talk will give an overview of the colliding worlds of big data and predictive analytics. It will do that by delving into the technologies and tools available in the market today that allow us to truly benefit from the barrage of data we are gathering at an ever-increasing pace.

Resources:

  1. Download the presentation slides
  2. Download the KNIME workflow used to generate a sample neural network for predicting churn
  3. Download the PMML file created during the demo




Friday, November 2, 2012

Predictive Solutions for Real-Time Scoring and Big Data with ADAPA and the Universal PMML Plug-in


PMML, the Predictive Model Markup Language, allows for predictive models to be easily moved into production and operationally deployed on-site, in the cloud, in-database or Hadoop. Zementis offers a range of products that make possible the deployment of predictive solutions and data mining models built in IBM SPSS, SAS, StatSoft STATISTICA, KNIME, KXEN, R, etc. Our products include the ADAPA Decisioning Engine and the Universal PMML Plug-in (UPPI). 



SOLUTIONS FOR REAL-TIME SCORING AND BIG DATA

ADAPA, the Babylonian god of wisdom, is the first PMML-based, real-time predictive decisioning engine available on the market, and the first scoring engine accessible on the Amazon Cloud and IBM SmartCloud as a service. ADAPA on the Cloud combines the benefits of Software as a Service (SaaS) with the scalability of cloud computing. ADAPA is also available as a traditional software license for deployment on site.

As even the god of wisdom knows, 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 these cases 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 at an accelerated pace. ADAPA’s sister product, the Universal PMML Plug-in (UPPI), is the Zementis solution for Hadoop and in-database scoring. UPPI is available for the IBM Netezza appliance, SAP Sybase IQ, and EMC Greenplum. It is also available for Hadoop/Datameer. 

BROAD SUPPORT FOR PREDICTIVE ANALYTICS AND PMML

ADAPA and UPPI consume model files that conform to the PMML standard, version 2.0 through 4.1. If your model development environment exports an older version of PMML, our products will automatically convert your file into a 4.1 compliant format. 

Our products support an extensive collection of statistical and data mining algorithms. These include:
  • Neural Networks (Back-Propagation, Radial-Basis Function, and Neural-Gas) 
  • Regression Models (Linear, Polynomial, and Logistic)
  • General Regression Models (General Linear, Ordinal Multinomial, Generalized Linear, Cox) 
  • Support Vector Machines (for regression and multi-class and binary classification) 
  • Decision Trees (for classification and regression)
  • Scorecards (including support for reason codes and complex attributes) 
  • Association Rules 
  • Ruleset Models (flat Decision Trees)
  • Clustering Models (Distribution-Based, Center-Based, and 2-Step Clustering) 
  • Naive Bayes Classifiers 
  • Multiple Models (model composition, chaining, segmentation, and ensemble - including Random Forest Models)
A myriad of functions for implementing data pre- and post-processing are also supported, including:
  • Value Mapping
  • Discretization
  • Normalization
  • Scaling
  • Logical and Arithmetic Operators
  • Conditional Logic
  • Built-in Functions
  • Lookup Tables
  • Business Decisions and Thresholds
  • Custom Functions ... and much much more
Contact us today! 

Zementis, Inc.
6125 Cornerstone Court East, Suite 250
San Diego, CA  92121
T: 619 330 0780  x2000

Visit us on the web: www.zementis.com
Follow us on twitter: @Zementis
Or send us an e-mail at info@zementis.com


Thursday, October 4, 2012

TOP 10 PMML Resources

These are the top 10 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 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) TALK/PRESENTATION: Our PMML talk at LinkedIn earlier this year to the ACM Data Mining Bay Area/SF group is available for on-demand viewing on YouTube. Slides are also available and cab be downloaded HERE.

3) 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.

4) 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.

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

6) 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.

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

8) EXAMPLES: In the DMG PMML Examples pagehttp://www.dmg.org/pmml_examples, you not only can find typical predictive models such as neural networks and decision trees, but also association rules and random forest models.

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

LinkedIn
10) DISCUSSION 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.

Tuesday, October 2, 2012

Big Data Made Easy with In-database Scoring

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 these cases 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.

A new day has come!

Zementis is now offering its amazing Universal PMML Plug-in™ (UPPI) for in-database scoring for the IBM Netezza appliance, SAP Sybase IQEMC Greenplum, Teradata and Teradata Aster.



Amazing! Why?

For starters, it won't break your budget (feel free to contact us for details). Also, it is simple to deploy and maintain. Our Universal PMML Plug-in was designed from the ground up to take advantage of efficient in-database execution. Last but not least, as its name suggests, it is PMML-based. PMML, the Predictive Model Markup Language is the standard for representing predictive models currently exported from all major commercial and open-source data mining tools. So, if you build your models in either SAS, IBM SPSS, STATISTICA, or R, you are ready to start benefiting from in-database scoring right away.

The PMML plugin seamlessly embeds models within your database. Data scoring requires nothing more than adding a simple function call into your SQL statements. You can score data against one model or against multiple models at the same time. There is no need to code complex data transformations and calculations in SQL or stored procedures. PMML and our Universal Plug-in can easily take care of that.

Modeling techniques currently supported are:



  • Neural Networks
  • Support Vector Machines
  • Naive Bayes Classifiers
  • Ruleset Models
  • Clustering Models (including Two-Step Clustering)
  • Decision Trees
  • Regression Models (including Cox Regression Models)
  • Scorecards (including reason codes)
  • Association Rules
  • Multiple Models (model composition, chaining, segmentation, and ensemble - including Random Forest models)

  • As well as extensive data pre- and post-processing capabilities.

    In addition to all these predictive techniques, UPPI accepts PMML models of all versions (2.0, 2.1, 3.0, 3.1, 3.2, 4.0, 4.1 and 4.2) generated by any of the major commercial and open source mining tools (SAS, SPSS/IBM, STATISTICA, MicroStrategy, Microsoft, Oracle, KXEN, Salford Systems, TIBCO, R/Rattle, KNIME, RapidMiner, etc.). It does not get more universal than this!


    Friday, September 28, 2012

    Scoring in the Cloud

    Moving a predictive model from the data scientist's desktop to the production environment is a "no brainer" with PMML, the Predictive Model Markup Language. Once expressed in PMML, a model can be operationally deployed in minutes. 

    ADAPA, the Zementis PMML-based scoring engine, allows for predictive models to be put to work in a host of different platforms and systems, including the IBM SmartCloud Enterprise. Since ADAPA is offered on the IBM SmartCloud as a service, users only pay for the service and the capacity on a monthly basis, eliminating the necessity for expensive software licenses and in-house hardware resources. 


    With PMML and ADAPA on the Cloud, one can deploy a predictive model in minutes anywhere in the world in any of the available data centers. The process of launching a virtual ADAPA server in the IBM SmartCloud corresponds to the traditional scenario of buying hardware and installing it in a server room. The only difference is that the server in this case sits in the cloud, comes with a preinstalled version of ADAPA, and launches in just a few minutes, on-demand and ready to be used. At any given time, you can have one or more instances running. Independent of processing power, each instance type provides a single-tenant architecture. The service is implemented as a private, dedicated instance that encapsulates predictive models and business rules. In this way, access (via HTTPS) to any instance is private. As a consequence, decision files and data never share the same engine with other clients.

    Open-standards and cloud computing make it easier for companies to tackle the big data challenge. Predictive analytics is finally delivering on its promise of transforming data into insights and value.

    Tuesday, September 25, 2012

    Representing Predictive Solutions with PMML

    Data mining scientists work hard to analyze historical data and to build the best predictive solutions out of it. IT engineers, on the other hand, are usually responsible for bringing these solutions to life, by recoding them into a format suitable for operational deployment. Given that data mining scientists and engineers tend to inhabit different information worlds, the process of moving a predictive solution from the scientist's desktop to the operational environment can get lost in translation and take months. The advent of data mining specific open standards such as the Predictive Model Markup Language (PMML) has turned this view upside down: the deployment of models can now be achieved by the same team who builds them, in a matter of minutes.

    In this talk to the ACM Data Mining Group, given at the LinkedIn auditorium in Sunnyvale, Dr. Alex Guazzelli not only provides the business rationale behind PMML, but also describes its main components. Besides being able to describe the most common modeling techniques, as of version 4.0, released in 2009, PMML is also capable of handling complex pre-processing tasks. As of version 4.1, released in December 2011, PMML has also incorporated complex post-processing to its structure as well as the ability to represent model ensemble, segmentation, chaining, and composition within a single language element. This combined representation power, in which an entire predictive solution (from pre-processing to model(s) to post-processing) can be represented in a single PMML file, attests to the language's refinement and maturity.
     

    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 the PMML conversion process so that it now converts PMML files from older versions to version 4.1. The conversion process is part of ADAPA and UPPI and is responsible for converting, correcting, and validating PMML files when these are uploaded for scoring.  
      


    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 23, 2012

    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:

    Enjoy!

    Tuesday, May 29, 2012

    Synergies and value proposition between IBM SPSS and ADAPA

    The ADAPA Decision Engine provides additional value to all your predictive assets. It is complimentary to IBM SPSS Modeler and IBM SPSS Statistics, since it extends these modeling environments into the IT operational domain.

    ADAPA is compatible with Modeler and Statistics through PMML, the Predictive Model Markup Language, which is the de facto standard to represent predictive models. PMML allows for models to be developed in one application and deployed on another, as long as both are PMML-compliant.

    Immediate benefits of using ADAPA


    Once a model built in any of the IBM SPSS tools is saved as a PMML file, it can be directly uploaded in ADAPA. With ADAPA, you can:
    • Execute your models independently of the IBM SPSS model development tool
    • Overcome any speed 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 other PMML-compliant model development tools such as R, KNIME, or SAS
    • Deploy your models in minutes, not months (no need for recoding models into production)
    • Manage models via Web Services or a Web console
    • Upload one or many models into ADAPA at once
    • Use rules to implement model segmentation
    • Benefit from the seamless integration of business rules and predictive models

    IBM SPSS PMML support


    IBM SPSS offers vast support for PMML through IBM SPSS Modeler (formerly known as Clementine) and Statistics. Both systems allow users to export a multitude of models in PMML (for details, click HERE). IBM products such as DB2 Intelligent Miner and ILOG JRules also offer support for PMML.

    A common industry standard


    PMML allows for the de-coupling of two very important modeling phases: development and operational deployment. With PMML, scientists can focus on data analysis and model building using the best of breed model development tools, whereas operational deployment and actual use of the model is made extremely easy and simple with ADAPA.

    ADAPA Solutions For

    For example, if a data mining scientist develops a decision tree model using IBM SPSS Modeler, all he/she needs to do to effectively deploy his/her model operationally is to save it as a PMML file and uploaded it in ADAPA. Once in ADAPA, the decision tree model is available for all to use, directly by business users and applications. It may be used by a business user directly from within Excel to score customers for a marketing campaign.

    By doing that, PMML allows for the model development environment to be used just for that, model development. Scoring, real-time or batch-mode from anywhere and at anytime, is handled by ADAPA.

    Friday, May 25, 2012

    Predictive Analytics at the Speed of Business

    Decision Management Solutions/Zementis Webinar (presented, May 3rd, 2012)

    Organizations are looking to maximize the value of their analytics investment. They need to accelerate the deployment process, reduce costs and get the analytic insight where they need it, when they need it. Increasingly organizations must deploy and manage many predictive models, use those models in real-time and integrate predictive analytics into a wide range of operational systems – in the cloud, on-premise, for Hadoop and in-database.

    In this webinar you will learn how Decision Management and ADAPA – a proven approach and real-time infrastructure – transform passive models into operational success. This webinar is jointly presented by James Taylor, CEO of Decision Management Solutions and Dr. Alex Guazzelli, Vice President of Analytics at Zementis.

    Presentation (on YouTube):





    Demo (on YouTube):






    Presentation and demo cover:
    • The current challenges in getting a return on your predictive analytic investment
    • The role of decision management in applying analytics when and where they are needed
    • The roles of predictive analytics and business rules technologies in decision management
    • How real-time infrastructure and rapid deployment maximizes analytic value
    • The importance of continuous monitoring and improvement in delivering ongoing results
    Decision Management Solutions & Zementis are leaders in Decision Management, providing consulting services in Decision Management, business rules and predictive analytics as well as a flexible platform for deploying predictive analytics on premise, in the cloud, for Hadoop or in-database.

    Download slides
     

    Monday, May 7, 2012

    ADAPA Demo: Seamless Integration of Predictive Analytics and Business Rules

    The Zementis ADAPA Decision Engine offers the perfect platform for moving complex solutions, which may include a combination of predictive analytics and business rules, from the development to the operational environment -- on the cloud, on-site, for Hadoop and in-database. 


    In this demo, Dr. Alex Guazzelli, VP of Analytics at Zementis, shows a pre-qualification app that uses predictive models and rules to analyze the risk of mortgage default on loan applications. An application is accepted or referred for a variety of loan products depending on its perceived risk. ADAPA is the engine driving this application in the back-end.

    Once logged in ADAPA, Dr. Guazzelli uses the ADAPA Web Console to download the mortgage solution files which are used throughout the demo. Predictive models expressed in PMML format are uploaded and verified in ADAPA along with rulesets expressed in tabular format. The ADAPA Web Console is used for managing predictive models, rulesets, and resource files as well as for batch-scoring. Real-time scoring is obtained via web-services or the Java API.

    Finally, Dr. Guazzelli shows how the ADAPA Add-in for Excel is used to score data directly from within Excel. This part of the demo features the scoring of loan and tax data as well as the visualization of results via dashboards.

    Monday, February 13, 2012

    PMML in Action - 2nd Edition is Out!

    This posting has been moved to the Zementis Support Site. You can still access it by clicking HERE.

    Wednesday, February 1, 2012

    ADAPA is now available on all Amazon Cloud regions

    This posting has been moved to the Zementis Support Site. You can still access it by clicking HERE.





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