Thursday, October 31, 2013

ADAPA on Amazon AWS Marketplace: Predictive Analytics and Big Data Scoring 1-click away


Clients benefit from our solutions by being able to use PMML, the Predictive Model Markup Language, to move their predictive models from IBM SPSS, R, SAS EM, ... and deploy them instantly in a variety of platforms, including the Amazon Elastic Compute Cloud (Amazon EC2).

ADAPA on the Amazon Cloud offers the power of our real-time PMML-based scoring engine on the Amazon Cloud. ADAPA on the Amazon Cloud comes pre-installed on a virtual server on the cloud. We call that an "ADAPA Instance".

The AWS (Amazon Web Services) Marketplace gives you the power of having ADAPA at your fingertips on three different types of virtual machines. Once you select the machine type and the cloud region in which you want it to run (US, Europe, Latin America or Asia-Pacific), all you need to select is 1-Click Launch and moments later your ADAPA instance is up and running, ready for deployment and execution. 


Big Data Scoring through ADAPA with S3 Processing

Zementis makes it super easy to score your big data by connecting your Amazon S3 (Simple Storage Service) bucket to your predictive models deployed in ADAPA on the Amazon Cloud. ADAPA with S3 Processing is intended for mission critical applications that require very high throughput of predictive analytics. While ADAPA provides real-time scoring via a Web-services API, S3 Processing addresses use cases with scoring requirements that involve tens or hundreds of millions of rows at a time.

Thursday, October 10, 2013

CIO Review: Zementis selected as one of the top 20 most promising big data companies

Selected by a distinguished panel comprising of CEOs, CIOs, VCs, industry analysts and the editorial board of CIO Review, Zementis has been named by CIO Review as one of the "Top 20 Most Promising Big Data Companies in 2013." Congratulations Zementis!

Read CIO Review - FULL ARTICLE


That comes as no surprise since Zementis is all about kicking down barriers for the fast deployment and execution of predictive solutions. By leveraging the PMML (Predictive Model Markup Language) standard, Zementis' products allow for predictive models built anywhere (IBM SPSS, KXEN, KNIME R, SAS, ...) to be deployed right-away on-site, in the cloud (Amazon, IBM, FICO), in-database (Pivotal/Greenplum, SAP Sybase IQ,  IBM PureData for Analytics/Netezza, Teradata and Teradata Aster) or in Hadoop (Hive or Datameer).


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. 


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.  

Zementis' PMML-based products: ADAPA for real-time scoring and UPPI for big data scoring, are designed from the ground up to deliver the agility necessary for models to be easily deployed in a variety of platforms and to be put to work right-away. 


Zementis ADAPA and UPPI kick-down the barriers for big data adoption!


Wednesday, October 2, 2013

R PMML Support: BetteR than EveR

How does it work? Simple! Once you build your model in R using any of the PMML supported model types, pass the model object as an input parameter to the pmml package as shown in the figure below.

pmml package

The pmml package offers export for a variety of model types, including:

   •   ksvm (kernlab): Support Vector Machines 
   •   nnet: Neural Networks 
   •   rpart: C&RT Decision Trees 
   •   lm & 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 (coming soon) 
   •   svm (e1071): Support Vector Machines (coming soon)

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

Data Transformations - the R pmmlTransformations Package

The pmmlTransformations package transforms data and, when used in conjunction with the pmml package, allows for data transformations to be exported together with the predictive model in a single PMML file. Transformations currently supported are:

   •   Min-max normalization 
   •   Z-score normalization 
   •   Dummy-fication of categorical variables 
   •   Value Mapping 
   •   Variable renaming

To learn more about this package, check out the paper we presented at the KDD 2013 PMML Workshop.





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