The ADAPA Decision Engine provides additional value to all your predictive assets. It is complimentary to R, since it extends your modeling environment into the IT operational domain.
ADAPA® is compatible with R through PMML, the Predictive Model Markup Language, which is the de facto standard to represent predictive models. PMML allows for models to be developed in one application and deployed on another, as long as both are PMML-compliant.
Immediate benefits of using ADAPA
Once a model built in R is saved as a PMML file, it can be directly uploaded in ADAPA. With ADAPA, you can:
- Execute your models independently of R
- Overcome memory and speed limitations imposed by R
- Produce scores in real-time (using Web Services or Java API), on-demand, or batch-mode
- Tap into all the advantages of cloud computing with ADAPA on Cloud (IBM SmartCloud or Amazon EC2)
- Execute your models directly from Excel, by using the ADAPA Add-in for Excel
- Benefit from using other PMML-compliant model development tools such as KNIME and RapidMiner
- Deploy your models in minutes, not months (no need for recoding models into production)
- Manage models via Web Services or a Web console
- Upload one or many models into ADAPA at once
- Use rules to implement model segmentation
- Benefit from the seamless integration of business rules and predictive models through PMML
R PMML support
R offers support for PMML through the R PMML Package available in CRAN. Zementis is a proud contributor to the PMML package which was featured on an article we wrote for The R Journal (to download article, click HERE). The PMML package allows users to export a multitude of predictive models in PMML (for details, click HERE).
We have put together a video which shows how easy it is to export PMML models from R. It uses a simple R script to build a decision tree model using rpart and exports it to PMML using the PMML package. To read posting and watch video, click HERE.
A common industry standard
PMML allows for the de-coupling of two very important modeling phases: development and operational deployment. With PMML, scientists can focus on data analysis and model building using the best of breed model development tools, whereas operational deployment and actual use of the model is made extremely easy and simple with ADAPA.
For example, if a data mining scientist develops a decision tree model using R rpart package, all he/she needs to do to effectively deploy his/her model operationally is to save it as a PMML file and uploaded it in ADAPA. Once in ADAPA, the decision tree model is available for all to use, directly by business users and applications. The model may be used by a business user directly from within Excel to score customers for a marketing campaign.
By doing that, PMML allows for the model development environment to be used just for that, model development. Scoring, real-time or batch-mode from anywhere and at anytime, is handled by ADAPA.