Cascading Pattern provides machine learning scoring algorithms and Predictive Model Markup Language (PMML) for applications on Apache Hadoop.
We have limited time to maintain this project, please contact us if interested in helping.
Pattern was created for developers who want to…
Quickly deploy machine scoring applications at scale on Apache Hadoop in as little as 4 lines of code
Leverage existing intellectual property in predictive models, and investments in predictive modeling tooling and core competencies
Accelerate application development and testing
Supported Model Types
Random Forest Algorithm
Quickly Deploy Predictive Models
Create your models in tools such as R, MicroStrategy and SAS, export those models in PMML, and then utilize Cascading Pattern to deploy them at scale.
How does Cascading Pattern work with R?
R is great for creating models, but it does not run efficiently on Hadoop. However, R does support PMML, a standards-based XML language for building and deploying sophisticated ensembles. So, you can export your model from R into PMML, and pass the PMML to Pattern to translate your model into a Cascading application.
Additionally, R works great with the Casading Lingual’s JDBC driver. Thus, you can pull data out of Hadoop and into R by using Lingual.
With Cascading Pattern and Cascading Lingual, the appropriate connections now exist between modeling tools and Hadoop for you to deploy your models on to Hadoop and pull data off of Hadoop for testing.
What is PMML?
Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define statistical and data mining models and to share models between PMML compliant applications.
PMML 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. It allows users to develop models within one vendor’s application, and use other 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.