The Zementis Python to PMML Converter (Py2PMML) provides an easy to use interface to translate your Python-generated machine learning models into PMML, the Predictive Model Markup Language standard. In particular, it allows for models built using scikit-learn to be consumed by the Zementis ADAPA
Once translated into PMML, models can be easily deployed and scored against new incoming data. For example, models can be deployed in ADAPA for real-time scoring or UPPI for Big Data scoring, in-database or Hadoop.
How does it work?
Easy! Once you build your model using the scikit-learn library in Python, all you need to to do is write out a text file containing the model parameters. This file is then used by Py2PMML to generate the corresponding PMML file for your model. With the PMML file in hand, you can simply deploy and execute it using one of the Zementis scoring engines in the operational platform of your choice.