Deploying models in production systems is generally a cumbersome process. If analysis is performed in a language like R or SAS, the coefficients of the model are usually extracted and translated to a "production-ready" language like R or Java.
However, this approach is flawed. The translation process is time consuming and error-prone. R is demonstrably capable of serving models in production environments as long as submillisecond latency is not a requirement. This means it should be possible to push analysis performed in R to directly score records in production systems without an intermediary. This significantly decreases the cost of iterating on machine learning models.
A tundraContainer is a simple bundling of the two critical components of any machine learning model.
The data preparation required to convert raw production data to a record that is acceptable to a trained classifier. For example, a regression-based model may need discretization of non-categorical variables or imputation of missing values.
The trained classifier, usually a native R S3 object with
a train method.
The former is provided by the mungebits2 package, while the latter is fully customizable to any R function. This approach allows arbitrary data preparation and statistical methods, unlike attempts such as PMML (Predictive Modeling Markup Language) which constrain the space of possible data preparation methodologies and statistical methodologies to a very limited subset.