In this webinar, we demonstrate through an industry specific use case how to identify patterns and relationships to make sound predictions using smart data analytics. You will learn best practices on:
Selecting the right machine learning approach for business and IT
Visualizing machine learning on Hadoop
Leveraging existing predictive algorithms on Hadoop
Model deployment used to be a big task. Predictive models, once built, needed to be re-coded into production to be able to score new data. This process was prone to errors and could easily take up to six months. Re-coding of predictive models has no place in the big data era we live in. Since data is changing rapidly, model deployment needs to be instantaneous and error-free.
PMML, the Predictive Model Markup Language, is the standard to represent predictive models. Given that PMML can be produced by all the top commercial and open-source data mining tools (e.g., FICO Model Builder, SAS EM, IBM SPSS, R, KNIME, ...), a predictive model can be easily moved into the production environment once it is represented as a PMML file.
Zementis offers ADAPA for real-time scoring and UPPI for big data scoring which make the entire model deployment process a no-brainer. Given that ADAPA and UPPI are universal PMML consumers (accept any version of PMML produced by any PMML-compliant tool), they can make predictive models instantly available for execution inside the production environment.
Big Data and Hadoop are somewhat synonymous terms these days, since the latter offers an important technological platform to tackle the challenge of analyzing large volumes of data. By the same token, predictive analytics is paramount for companies to extract value and insight from big data. It is in this context that Zementis brings its standards-based predictive scoring engine into a variety of Big Data platforms, including the cloud as well as in-database. By offering the Universal PMML Plug-in (UPPI) for Hadoop, Zementis takes a big step in making its technology available for companies around the globe to easily deploy, execute, and integrate scalable standards-based predictive analytics on a massive parallel scale through the use of Hive, a data warehouse system for Hadoop, and Datameer, an end-to-end BI solution that works on top of Hadoop.
UPPI brings together essential technologies, offering the best combination of open standards and scalability for the application of predictive analytics. It 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 for Hadoop/Hive
Hive makes it possible for large datasets stored in Hadoop compatible systems to be easily analyzed. Since it provides a mechanism to project structure onto the data, Hive allows for queries to be made using a SQL-like language called HiveQL.
Once deployed in UPPI, predictive models turn into UDFs (User-defined Functions). These can then be invoked directly in HiveQL. In this way, UPPI offers Hadoop users the best combination of open standards and scalability for the application of predictive analytics.
UPPI for Hadoop/Hive delivers instant and scalable scoring for Big Data while retaining compatibility with most major data mining tools through the PMML Standard. It also brings brings the scalability of Hadoop to the execution of predictive analytics.
UPPI for Datameer
Zementis and Datameer have partnered to deliver standards-based execution of predictive analytics on a massive parallel scale. This joint solution combines the Zementis plug-in for 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.
Datameer uses Apache Hadoop, a Java-based framework that supports the parallel storage and processing of large data sets in a distributed environment, as its back-end storage and processing engine to scale cost-effectively to 4000 servers and petabytes of data. It provides wizard-based data integration to integrate large datasets of structured and unstructured data, integrated analytics with familiar spreadsheet-like interface and over 200 built-in analytic functions and drag and drop reporting and dashboarding visualization for end-users. Open API's for data integration, analytics and dashboarding make it easy to access custom data sources, utilize advanced or custom analytics like predictive modeling as well as custom visualizations.
Predictive Scoring for Hadoop - Advantages
UPPI for Datameer delivers instant and scalable scoring for Big Data while retaining compatibility with most major data mining tools through the PMML Standard. Through its versatile deployment solution, the Zementis/Datameer partnership:
Brings the scalability of Hadoop to the execution of predictive analytics
Supports PMML to avoid time-consuming and expensive one-off predictive analytics projects
Integrates data from multiple data sources and formats without complex data and schema mappings that are time consuming to set up and difficult to change
Provides cost effective storage and processing of large volumes of highly granular data that predictive applications often require
Brings together a 100% standards-based approach to analytics that lowers total cost of ownership and increases reuse control and flexibility for orchestrating critical day-to-day business decisions.
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:
Download the KNIME workflow used to generate a sample neural network for predicting churn
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.
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 our PMML conversion process so that it now converts PMML files from older versions to version 4.1. In this way, every time a PMML file is presented to ADAPA or UPPI, it is automatically converted to PMML 4.1.
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.
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
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
Join us for the 2012 Hadoop Summit at the San Jose Convention Center on June 13-14.
Ulrich Rueckert, Data Scientist at Datameer and Michael Zeller, Zementis CEO, will be presenting on Wednesday, June 13, 1:30-2:10 pm.
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.
This joint Datameer/Zementis presentation will outline 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 will showcase how Datameer and the Zementis Universal PMML Plug-in take advantage of a highly parallel Hadoop architecture to efficiently derive predictions from very large volumes of data.
Session atendees will learn:
How to leverage predictive analytics in the context of big data
Introduction to the Predictive Model Markup Language (PMML) open standard for data mining
How to reduce cost and complexity of predictive analytics
