Zementis provides a platform for the deployment of predictive models in PMML format (Predictive Model Markup Language). Big data (Hadoop) and other database platforms are supported.
Data scientists using R can translate their models into PMML, which can then be deployed in a production environment using Zementis platforms. Many of the most common modeling techniques are supported, including:
- ksvm (kernlab): Support Vector Machines
- nnet: Neural Networks
- rpart: C&RT Decision Trees
- lm & glm (stats): Linear and Binary Logistic Regression Models
- arules: Association Rules
- kmeans and hclust: Clustering Models
- multinom (nnet): Multinomial Logistic Regression Models
- glm (stats): Generalized Linear Models for classification and regression with a wide variety of link functions
- randomForest: Random Forest Models for classification and regression
- coxph (survival): Cox Regression Models to calculate survival and stratified cumulative hazards
- naiveBayes (e1071): Naive Bayes Classifiers
- glmnet: Linear ElasticNet Regression Models
- ada: Stochastic Boosting (coming soon)
- svm (e1071): Support Vector Machines (coming soon)
The basic architecture is shown in the diagram below:
