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BigML creates a marketplace for Predictive Models

BigML has created a marketplace for selling Datasets and Models. This is a first (?) as the closest market for Predictive Analytics till now was Rapid Miner’s marketplace for extensions (at http://rapidupdate.de:8180/UpdateServer/faces/index.xhtml)

From http://blog.bigml.com/2012/10/25/worlds-first-predictive-marketplace/

SELL YOUR DATA

You can make your Dataset public. Mind you: the Datasets we are talking about are BigML’s fancy histograms. This means that other BigML users can look at your Dataset details and create new models based on this Dataset. But they can not see individual records or columns or use it beyond the statistical summaries of the Dataset. Your Source will remain private, so there is no possibility of anyone accessing the raw data.

SELL YOUR MODEL

Now, once you have created a great model, you can share it with the rest of the world. For free or at any price you set.Predictions are paid for in BigML Prediction Credits. The minimum price is ‘Free’ and the maximum price indicated is 100 credits.

White Box Models

Clicking on the white open lock will open up your model to the rest of the world. Anyone can now buy your model, explore it, use it to make predictions

Black Box Models

If you choose the black box setting (the black open lock icon), other BigML users will NOT be able to view or clone your model, but they will be able to use it to make predictions.

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DOWNLOAD YOUR MODEL

BigML.com have added downloads to our models. Simply choose the format you want and you can copy/paste the code or text. There is a range of formats that they offer currently: JSON PML, PMML, Python, Ruby, Objective-C, Java, the rules of the decision tree in plain text and a Summary overview of your model. Around the corner are MS Excel downloads and R (of course!).

PUBLICIZE YOUR MODEL

There’s also an ‘embed’ function, so now you can embed the little poster of your model in your blog post or website, so it is easy to share it in your own environment.

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It is nice to see Models and Data getting the APPY treatment and hopefully, it will encourage other vendors Iike Google Prediction API etc to further spend thought and effort to reward data mining individuals directly without going through corporate intermediaries while ensuring intellectual property safeguards .

An R package market for enterprises? for Python libraries? JMP addins? A market for SAS Macros- who knows what the future shall hold. But overall, this is a very positive step by the BigML.com team. The App marketplace has helped revolutionize mobile and desktop computing and hopefully it will do the same for Business Analytics.

 

Predictive Models Ain’t Easy to Deploy

 

This is a guest blog post by Carole Ann Matignon of Sparkling Logic. You can see more on Sparkling Logic at http://my.sparklinglogic.com/

Decision Management is about combining predictive models and business rules to automate decisions for your business. Insurance underwriting, loan origination or workout, claims processing are all very good use cases for that discipline… But there is a hiccup… It ain’t as easy you would expect…

What’s easy?

If you have a neat model, then most tools would allow you to export it as a PMML model – PMML stands for Predictive Model Markup Language and is a standard XML representation for predictive model formulas. Many model development tools let you export it without much effort. Many BRMS – Business rules Management Systems – let you import it. Tada… The model is ready for deployment.

What’s hard?

The problem that we keep seeing over and over in the industry is the issue around variables.

Those neat predictive models are formulas based on variables that may or may not exist as is in your object model. When the variable is itself a formula based on the object model, like the min, max or sum of Dollar amount spent in Groceries in the past 3 months, and the object model comes with transaction details, such that you can compute it by iterating through those transactions, then the problem is not “that” big. PMML 4 introduced some support for those variables.

The issue that is not easy to fix, and yet quite frequent, is when the model development data model does not resemble the operational one. Your Data Warehouse very likely flattened the object model, and pre-computed some aggregations that make the mapping very hard to restore.

It is clearly not an impossible project as many organizations do that today. It comes with a significant overhead though that forces modelers to involve IT resources to extract the right data for the model to be operationalized. It is a heavy process that is well justified for heavy-duty models that were developed over a period of time, with a significant ROI.

This is a show-stopper though for other initiatives which do not have the same ROI, or would require too frequent model refresh to be viable. Here, I refer to “real” model refresh that involves a model reengineering, not just a re-weighting of the same variables.

For those initiatives where time is of the essence, the challenge will be to bring closer those two worlds, the modelers and the business rules experts, in order to streamline the development AND deployment of analytics beyond the model formula. The great opportunity I see is the potential for a better and coordinated tuning of the cut-off rules in the context of the model refinement. In other words: the opportunity to refine the strategy as a whole. Very ambitious? I don’t think so.

About Carole Ann Matignon

http://my.sparklinglogic.com/index.php/company/management-team

Carole-Ann Matignon Print E-mail

Carole-Ann MatignonCarole-Ann Matignon – Co-Founder, President & Chief Executive Officer

She is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry.  Her claim to fame is managing the strategy and direction of Blaze Advisor, the leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience.  She speaks often at Industry conferences and has conducted university classes in France and Washington DC.

She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication. At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs around change management.

While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM). She developed a growing interest in Optimization as well as Business Rules. At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart. She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).

Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business. Her technical background kept her very much in touch with technology as she advanced.

PMML Augustus

Here is a new-old system in open source for

for building and scoring statistical models designed to work with data sets that are too large to fit into memory.

http://code.google.com/p/augustus/

Augustus is an open source software toolkit for building and scoring statistical models. It is written in Python and its
most distinctive features are:
• Ability to be used on sets of big data; these are data sets that exceed either memory capacity or disk capacity, so
that existing solutions like R or SAS cannot be used. Augustus is also perfectly capable of handling problems
that can fit on one computer.
• PMML compliance and the ability to both:
– produce models with PMML-compliant formats (saved with extension .pmml).
– consume models from files with the PMML format.
Augustus has been tested and deployed on serveral operating systems. It is intended for developers who work in the
financial or insurance industry, information technology, or in the science and research communities.
Usage
Augustus produces and consumes Baseline, Cluster, Tree, and Ruleset models. Currently, it uses an event-based
approach to building Tree, Cluster and Ruleset models that is non-standard.

New to PMML ?

Read on http://code.google.com/p/augustus/wiki/PMML

The Predictive Model Markup Language or PMML is a vendor driven XML markup language for specifying statistical and data mining models. In other words, it is an XML language so that (more…)

Predictive Analytics World Conference –New York City and London, UK

Please use the following code  to get a 15% discount on the 2 Day Conference Pass:  AJAYNY11.

Predictive Analytics World Conference –New York City and London, UK

October 17-21, 2011 – New York City, NY (pawcon.com/nyc)
Nov 30 – Dec 1, 2011 – London, UK (pawcon.com/london)

Predictive Analytics World (pawcon.com) is the business-focused event for predictive analytics
professionals, managers and commercial practitioners, covering today’s commercial deployment of
predictive analytics, across industries and across software vendors. The conference delivers case
studies, expertise, and resources to achieve two objectives:

1) Bigger wins: Strengthen the business impact delivered by predictive analytics

2) Broader capabilities: Establish new opportunities with predictive analytics

Case Studies: How the Leading Enterprises Do It

Predictive Analytics World focuses on concrete examples of deployed predictive analytics. The leading
enterprises have signed up to tell their stories, so you can hear from the horse’s mouth precisely how
Fortune 500 analytics competitors and other top practitioners deploy predictive modeling, and what
kind of business impact it delivers.

PAW NEW YORK CITY 2011

PAW’s NYC program is the richest and most diverse yet, featuring over 40 sessions across three tracks
- including both X and Y tracks, and an “Expert/Practitioner” track — so you can witness how predictive
analytics is applied at major companies.

PAW NYC’s agenda covers hot topics and advanced methods such as ensemble models, social data,
search marketing, crowdsourcing, blackbox trading, fraud detection, risk management, survey analysis,
and other innovative applications that benefit organizations in new and creative ways.

WORKSHOPS: PAW NYC also features five full-day pre- and post-conference workshops that
complement the core conference program. Workshop agendas include advanced predictive modeling
methods, hands-on training, an intro to R (the open source analytics system), and enterprise decision
management.

For more see http://www.predictiveanalyticsworld.com/newyork/2011/

PAW LONDON 2011

PAW London’s agenda covers hot topics and advanced methods such as risk management, uplift
(incremental lift) modeling, open source analytics, and crowdsourcing data mining. Case study
presentations cover campaign targeting, churn modeling, next-best-offer, selecting marketing channels,
global analytics deployment, email marketing, HR candidate search, and other innovative applications
that benefit organizations in new and creative ways.

Join PAW and access the best keynotes, sessions, workshops, exposition, expert panel, live demos,
networking coffee breaks, reception, birds-of-a-feather lunches, brand-name enterprise leaders, and

industry heavyweights in the business.

For more see http://www.predictiveanalyticsworld.com/london

CROSS-INDUSTRY APPLICATIONS

Predictive Analytics World is the only conference of its kind, delivering vendor-neutral sessions across
verticals such as banking, financial services, e-commerce, education, government, healthcare, high
technology, insurance, non-profits, publishing, social gaming, retail and telecommunications

And PAW covers the gamut of commercial applications of predictive analytics, including response
modeling, customer retention with churn modeling, product recommendations, fraud detection, online
marketing optimization, human resource decision-making, law enforcement, sales forecasting, and
credit scoring.

Why bring together such a wide range of endeavors? No matter how you use predictive analytics, the
story is the same: Predicatively scoring customers optimizes business performance. Predictive analytics
initiatives across industries leverage the same core predictive modeling technology, share similar project
overhead and data requirements, and face common process challenges and analytical hurdles.

RAVE REVIEWS:

“Hands down, best applied, analytics conference I have ever attended. Great exposure to cutting-edge
predictive techniques and I was able to turn around and apply some of those learnings to my work
immediately. I’ve never been able to say that after any conference I’ve attended before!”

Jon Francis
Senior Statistician
T-Mobile

Read more: Articles and blog entries about PAW can be found at http://www.predictiveanalyticsworld.com/
pressroom.php

VENDORS. Meet the vendors and learn about their solutions, software and service. Discover the best
predictive analytics vendors available to serve your needs – learn what they do and see how they
compare

COLLEAGUES. Mingle, network and hang out with your best and brightest colleagues. Exchange
experiences over lunch, coffee breaks and the conference reception connecting with those professionals
who face the same challenges as you.

GET STARTED. If you’re new to predictive analytics, kicking off a new initiative, or exploring new ways
to position it at your organization, there’s no better place to get your bearings than Predictive Analytics
World. See what other companies are doing, witness vendor demos, participate in discussions with the
experts, network with your colleagues and weigh your options!

For more information:
http://www.predictiveanalyticsworld.com

View videos of PAW Washington DC, Oct 2010 — now available on-demand:

http://www.predictiveanalyticsworld.com/online-video.php

What is predictive analytics? See the Predictive Analytics Guide:
http://www.predictiveanalyticsworld.com/predictive_analytics.php

If you’d like our informative event updates, sign up at:

http://www.predictiveanalyticsworld.com/signup-us.php

To sign up for the PAW group on LinkedIn, see:
http://www.linkedin.com/e/gis/1005097

For inquiries e-mail regsupport@risingmedia.com or call (717) 798-3495.

Augustus- a PMML model producer and consumer. Scoring engine.

A Bold GNU Head

Image via Wikipedia

I just checked out this new software for making PMML models. It is called Augustus and is created by the Open Data Group (http://opendatagroup.com/) , which is headed by Robert Grossman, who was the first proponent of using R on Amazon Ec2.

Probably someone like Zementis ( http://adapasupport.zementis.com/ ) can use this to further test , enhance or benchmark on the Ec2. They did have a joint webinar with Revolution Analytics recently.

https://code.google.com/p/augustus/

Recent News

  • Augustus v 0.4.3.1 has been released
  • Added a guide (pdf) for including Augustus in the Windows System Properties.
  • Updated the install documentation.
  • Augustus 2010.II (Summer) release is available. This is v 0.4.2.0. More information is here.
  • Added performance discussion concerning the optional cyclic garbage collection.

See Recent News for more details and all recent news.

Augustus

Augustus is a PMML 4-compliant scoring engine that works with segmented models. Augustus is designed for use with statistical and data mining models. The new release provides Baseline, Tree and Naive-Bayes producers and consumers.

There is also a version for use with PMML 3 models. It is able to produce and consume models with 10,000s of segments and conforms to a PMML draft RFC for segmented models and ensembles of models. It supports Baseline, Regression, Tree and Naive-Bayes.

Augustus is written in Python and is freely available under the GNU General Public License, version 2.

See the page Which version is right for me for more details regarding the different versions.

PMML

Predictive Model Markup Language (PMML) is an XML mark up language to describe statistical and data mining models. PMML describes the inputs to data mining models, the transformations used to prepare data for data mining, and the parameters which define the models themselves. It is used for a wide variety of applications, including applications in finance, e-business, direct marketing, manufacturing, and defense. PMML is often used so that systems which create statistical and data mining models (“PMML Producers”) can easily inter-operate with systems which deploy PMML models for scoring or other operational purposes (“PMML Consumers”).

Change Detection using Augustus

For information regarding using Augustus with Change Detection and Health and Status Monitoring, please see change-detection.

Open Data

Open Data Group provides management consulting services, outsourced analytical services, analytic staffing, and expert witnesses broadly related to data and analytics. It has experience with customer data, supplier data, financial and trading data, and data from internal business processes.

It has staff in Chicago and San Francisco and clients throughout the U.S. Open Data Group began operations in 2002.


Overview

The above example contains plots generated in R of scoring results from Augustus. Each point on the graph represents a use of the scoring engine and a chart is an aggregation of multiple Augustus runs. A Baseline (Change Detection) model was used to score data with multiple segments.

Typical Use

Augustus is typically used to construct models and score data with models. Augustus includes a dedicated application for creating, or producing, predictive models rendered as PMML-compliant files. Scoring is accomplished by consuming PMML-compliant files describing an appropriate model. Augustus provides a dedicated application for scoring data with four classes of models, Baseline (Change Detection) ModelsTree ModelsRegression Models and Naive Bayes Models. The typical model development and use cycle with Augustus is as follows:

  1. Identify suitable data with which to construct a new model.
  2. Provide a model schema which proscribes the requirements for the model.
  3. Run the Augustus producer to obtain a new model.
  4. Run the Augustus consumer on new data to effect scoring.

Separate consumer and producer applications are supplied for Baseline (Change Detection) models, Tree models, Regression models and for Naive Bayes models. The producer and consumer applications require configuration with XML-formatted files. The specification of the configuration files and model schema are detailed below. The consumers provide for some configurability of the output but users will often provide additional post-processing to render the output according to their needs. A variety of mechanisms exist for transmitting data but user’s may need to provide their own preprocessing to accommodate their particular data source.

In addition to the producer and consumer applications, Augustus is conceptually structured and provided with libraries which are relevant to the development and use of Predictive Models. Broadly speaking, these consist of components that address the use of PMML and components that are specific to Augustus.

Post Processing

Augustus can accommodate a post-processing step. While not necessary, it is often useful to

  • Re-normalize the scoring results or performing an additional transformation.
  • Supplements the results with global meta-data such as timestamps.
  • Formatting of the results.
  • Select certain interesting values from the results.
  • Restructure the data for use with other applications.

PMML Plugin for Greenplum now available

Predictive Model Markup Language

Image via Wikipedia

From a press release from Zementis.

 

, the Universal PMML Plug-in for in-database scoring. Available now for the EMC Greenplum Database, a high-performance massively parallel processing (MPP) database, the plug-in leverages the Predictive Model Markup Language (PMML) to execute predictive models directly within EMC Greenplum, for highly optimized in-database scoring.

Universal PMML Plug-in

Developed by the Data Mining Group (DMG), PMML is supported by all major data mining vendors, e.g., IBM SPSS, SAS, Teradata, FICO, STASTICA, Microstrategy, TIBCO and Revolution Analytics as well as open source tools like R, KNIME and RapidMiner. With PMML, models built in any of these data mining tools can now instantly be deployed in the EMC Greenplum database. The net result is the ability to leverage the power of standards-based predictive analytics on a massive scale, right where the data resides.

“By partnering with Zementis, a true PMML innovator, we are able to offer a vendor-agnostic solution for moving enterprise-level predictive analytics into the database execution environment,” said Dr. Steven Hillion, Vice President of Analytics at EMC Greenplum. “With Zementis and PMML, the de-facto standard for representing data mining models, we are eliminating the need 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.”

Want to learn more?
 

To learn more about how the EMC Greenplum Database and the Universal PMML Plug-in work together, feel free to:

  1. Visit the PMML Plug-in product page
  2. Download the white paper

The Universal PMML Plug-in for the EMC Greenplum Database is available now. Contact us today for more information.

Michael Zeller, CEO, Zementis

 

 

Zementis partners with R Analytics Vendor- Revo

Logo for R

Image via Wikipedia

Just got a  PR email from Michael Zeller,CEO , Zementis annoucing Zementis (ADAPA) and Revolution  Analytics just partnered up.

Is this something substantial or just time-sharing http://bi.cbronline.com/news/sas-ceo-says-cep-open-source-and-cloud-bi-have-limited-appeal or a Barney Partnership (http://www.dbms2.com/2008/05/08/database-blades-are-not-what-they-used-to-be/)

Summary- Thats cloud computing scoring of models on EC2 (Zementis) partnering with the actual modeling software in R (Revolution Analytics RevoDeployR)

See previous interviews with both Dr Zeller at http://decisionstats.com/2009/02/03/interview-michael-zeller-ceozementis/ ,http://decisionstats.com/2009/05/07/interview-ron-ramos-zementis/ and http://decisionstats.com/2009/10/05/interview-michael-zellerceo-zementis-on-pmml/)

and Revolution guys at http://decisionstats.com/2010/08/03/q-a-with-david-smith-revolution-analytics/

and http://decisionstats.com/2009/05/29/interview-david-smith-revolution-computing/

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strategic partnership with Revolution Analytics, the leading commercial provider of software and support for the popular open source R statistics language. With this partnership, predictive models developed on Revolution R Enterprise are now accessible for real-time scoring through the ADAPA Decisioning Engine by Zementis. 

ADAPA is an extremely fast and scalable predictive platform. Models deployed in ADAPA are automatically available for execution in real-time and batch-mode as Web Services. ADAPA allows Revolution R Enterprise to leverage the Predictive Model Markup Language (PMML) for better decision management. With PMML, models built in R can be used in a wide variety of real-world scenarios without requiring laborious or expensive proprietary processes to convert them into applications capable of running on an execution system.

partnership

“By partnering with Zementis, Revolution Analytics is building an end-to-end solution for moving enterprise-level predictive R models into the execution environment,” said Jeff Erhardt, Revolution Analytics Chief Operation Officer. “With Zementis, we are eliminating the need to take R applications apart and recode, retest and redeploy them in order to obtain desirable results.”

 

Got demo? 

Yes, we do! Revolution Analytics and Zementis have put together a demo which combines the building of models in R with automatic deployment and execution in ADAPA. It uses Revolution Analytics’ RevoDeployR, a new Web Services framework that allows for data analysts working in R to publish R scripts to a server-based installation of Revolution R Enterprise.

Action Items:

  1. Try our INTERACTIVE DEMO
  2. DOWNLOAD the white paper
  3. Try the ADAPA FREE TRIAL

RevoDeployR & ADAPA allow for real-time analysis and predictions from R to be effectively used by existing Excel spreadsheets, BI dashboards and Web-based applications, all in real-time.

RevoADAPAPredictive analytics with RevoDeployR from Revolution Analytics and ADAPA from Zementis put model building and real-time scoring into a league of their own. Seriously!

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