Software AG is aiming to bring the burgeoning world of artificial intelligence, predictive analytics, machine learning and deep learning in line with the statistical and data mining industry standard of the Predictive Model Markup Language (PMML).

is a Python library, compatible with Python 3.5+, which features full support for the PMML XML-based predictive model and provides data preprocessing, script execution and deep neural networks through extensions. Through , Software AG explains users can export PMML implementations of a large number from popular machine learning and deep learning Python frameworks. For any that aren’t supported out of the box, users can create their own exporters.

“Besides about 500 Python classes which each cover a PMML tag and all constructor parameters/attributes as defined in the standard, Nyoka also provides an increasing number of convenience classes and functions that make the Data Scientist’s life easier for example by reading or writing any PMML file in one line of code from within your favorite Python environment,” the company wrote on the ’s GitHub repository. “Nyoka comes to you with the complete source code in Python, an extended HTML documentation for the classes/functions and a growing number of Jupyter Notebook tutorials that help you familiarizing yourself with the way how Nyoka supports you to use PMML as your favorite Data Science transport file format.”

The project aims to make the life of data scientists easier by making deployment of AI solutions like Software AG’s own Zementis predictive analysis more accessible via a well-known, industry-standard framework.

“To address the challenges that organizations face with complex AI solutions, frequent model updates, cross-platform execution as well as data integration, Software AG emphasizes a vendor neutral approach that provides users plug-and-play simplicity with a wide range of components,” Michael Zeller, senior vice president of AI strategy and innovation with Software AG, said in the release announcement. “Nyoka streamlines the work of data scientists, reduces the complexities of deploying machine learning models, and gives them more time to focus on creating new models that deliver increased business value.”



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