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.

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