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.

Wednesday, May 28, 2014

Creating, Modifying, Deploying and Scoring Predictive Models with PMML, ADAPA and KNIME

Zementis and KNIME co-presented a webinar on creating, modifying, deploying and scoring predictive models using PMML. The webinar is now available for viewing on-demand (see below). It starts with Iris Adae from KNIME giving an overview of PMML, the Predictive Model Markup Language standard as well as the extensive support KNIME offers for PMML. PMML is the de facto standard for predictive analytics and can be produced by KNIME for a number of modeling techniques as well as data pre-processing nodes/computations.

Iris' presentation and demo are then followed by Alex Guazzelli from Zementis who shows how easy it is for anyone to benefit from models built in KNIME or R and deployed in the Zementis ADAPA Scoring Engine for execution. Once uploaded in ADAPA, models are available for scoring via web-services (SOAP or REST). KNIME can then be used to connect to a database, read in data and pass it through ADAPA for scoring via the REST API.

 On-demand webinar (available on YouTube):

 

The idea presented in this webinar is to show how easy one can easily move a predictive model from the scientist's desktop to the IT operational environment. When training a model, scientists rely on historical data, but when using the model on a regular basis, the model is moved or deployed in production where it presented with new data. ADAPA provides a scalable and lightning fast scoring engine for models that live in production. And, although KNIME data mining nodes are typically used by scientists to build models, its database and REST nodes as well as PMML-enabled nodes can simply be used to create a flow for passing models and data for scoring in ADAPA. 

Use-cases discussed are:

  • Read data from a flat file, use KNIME for data pre-processing and building of a neural network model. Export the entire predictive workflow as a PMML file and then take this PMML file and upload and score it in ADAPA via its Admin Web Console. 
  • Read data from a database (MySQL, SQLServer, Oracle, ...), build model in KNIME, export model as a PMML file and deploy it in ADAPA using its REST API. This use-case also shows new or testing data flowing from the database and into ADAPA for scoring via a sequence of KNIME nodes. 
  • The video also shows a case in which one can use KNIME nodes to simply read a PMML file produced in any PMML-compliant data mining tool (R, SAS EM, SPSS, ...), upload it in ADAPA using the REST API and score new data from MySQL in ADAPA also through the REST interface. Note that in this case, the model has already been trained and we are just using KNIME to deploy the existing PMML file in ADAPA for scoring. 

 To watch the Zementis discussed use-cases only, watch:

Tuesday, May 27, 2014

Zementis and SAP: HANA Marketplace, SAP Blog - Interview, Big Data Bus

The Zementis partnership with SAP is manifesting itself in a number of ways. 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.




Thursday, May 15, 2014

Transforming R to PMML: Zementis Presentation to the Bay Area R Users Group

The Zementis team was honored to give a presentation this week (May 12) to the Bay Area R Users Group
Our talk addressed how to convert predictive models developed in R to PMML, the Predictive Model Markup Language standard. We described the pmml and pmmlTransformations packages (see details below) and discussed the benefits of doing so which include:
  • Overcoming R's memory and speed limitations 
  • Deploying predictive models built in R in minutes, not months
  • Making many predictive models operational at once
  • Using PMML multiple models element to deploy ensembles, segmentation, and chaining 
In our presentation, we also discussed how Zementis' technology not only enables models to work with RDMS and NOSQL databases but also how it enables real-time scoring against in-flight data.

R PMML Package


A PMML package for R that exports all kinds of predictive models is available directly from CRAN.
The pmml package offers support for the following data mining algorithms:
  • ksvm (kernlab): Support Vector Machines
  • nnet: Neural Networks
  • rpart: C&RT Decision Trees 
  • lm and glm (stats): Linear and Binary Logistic Regression Models 
  • arules: Association Rules
  • kmeans and hclust: Clustering Models
  • multinom (nnet): Multinomial Logistic Regression Models
  • glm (stats): Generalized Linear Models for classification and regression with a wide variety of link functions 
  • randomForest: Random Forest Models for classification and regression
  • coxph (survival): Cox Regression Models to calculate survival and stratified cumulative hazards
  • naiveBayes (e1071): Naive Bayes Classifiers
  • glmnet: Linear ElasticNet Regression Models
  • ada: Stochastic Boosting
  • svm (e1071): Support Vector Machines

The pmml package can also export data transformations built with the pmmlTransformations package (see below). It can also be used to merge two disctinct PMML files into one. For example, if transformations and model were saved into separate PMML files, it can combine both files into one, as described in Chapter 5 of the PMML book - PMML in Action.

How does it work?


Simple, once you build your model using any of the supported model types, pass the model object as an input parameter to the pmml function as shown in the figure below:



Example - sequence of R commands used to build a linear regression model using lm and the Iris dataset:


Documentation


For more on the pmml package, please take a look at the paper we published in The R Journal. For that, just follow the link below:
Also, make sure to check out the package's documentation from CRAN:

2) CRAN: pmml Package

R PMML Transformations Package


This is a brand new R package. Called pmmlTranformations, this package transforms data and when used in conjunction with the pmml package, it allows for data transformations to be exported together with the predictive model in a single PMML file. Transformations currently supported include:
  • Min-max normalization
  • Z-score normalization
  • Dummy-fication of categorical variables
  • Value Mapping
  • Discretization (binning)
  • Variable renaming

If you would like to contribute code to the pmmlTransformations package, please feel free to contact us.

How does it work?


The pmmlTransformations package works in tandem with the pmml package so that data pre-processing can be represented together with the model in the resulting PMML code. 

In R, as shown in the figure below, this process includes three steps:

  1. With the use of the pmmlTransformations package, transform the raw input data as appropriate
  2. Use transformed and raw data as inputs to the modeling function/package (hclust, nnet, glm, ...)
  3. Output the entire solution (data pre-processing + model) in PMML using the pmml package


Example - sequence of R commands used to build a linear regression model using lm with transformed data


Documentation


For more on the pmmlTransformations package, please take a look at the paper we wrote for the KDD 2013 PMML Workshop. For that, just follow the link below:
Also, make sure to check out the package's documentation from CRAN:






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