Predictive Analytics Toolkit: Open Standards and Cloud Computing
Organizations around the globe increasingly recognize the value that predictive analytics offers to their business. The complexity of development, integration, and deployment of predictive models, however, is often considered cost-prohibitive for many projects. In light of mature open source solutions, open standards, and SOA principles we offer an agile model development life cycle that allows us to quickly leverage predictive analytics in operational environments.
Starting with data analysis and model development, you can effectively use the Predictive Model Markup Language (PMML) standard, to move complex decision models from the scientist’s desktop into a scalable production environment hosted on the Amazon Elastic Compute Cloud (Amazon EC2).
Expressing Models in PMML
PMML is an XML-based language used to define predictive models. It was specified by the Data Mining Group (DMG), an independent group of leading technology companies including Zementis. By providing a uniform standard to represent such models, PMML allows for the exchange of predictive solutions between different applications and various vendors.
Open source statistical tools such as R can be used to develop data mining models based on historical data. R allows for models to be exported into PMML which can then be imported into an operational decision platform and be ready for production use in a matter of minutes.
On-Demand Predictive Analytics
Amazon EC2 is a reliable, on-demand infrastructure on which we offer the ADAPA Predictive Decisioning Engine based on the Software as a Service (SaaS) paradigm. ADAPA imports models expressed in PMML and executes these in batch mode, or real-time via web-services.
Our service is implemented as a private, dedicated Amazon EC2 instance of ADAPA. Each client has access to his/her own ADAPA instance via HTTP/HTTPS. In this way, models and data for one client never share the same engine with other clients.
Using a SaaS solution to break down traditional barriers that currently slow the adoption of predictive analytics, our strategy translates predictive models into operational assets with minimal deployment costs and leverages the inherent scalability of utility computing.
In summary, ADAPA allows for:
- Cost-effective and reliable service based on Amazon’s EC2 infrastructure
- Secure execution of predictive models through dedicated and controlled instances including HTTPS and Web-Services security
- On-demand computing. Choice of instance type (small, large, extra-large, …) and launch of multiple instances.
- Superior time-to-market by providing rapid deployment of predictive models and an agile enterprise decision management environment.
For a practical guide, watch:
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