From the course: DevOps for Data Scientists
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Predictive Model Markup Language
From the course: DevOps for Data Scientists
Predictive Model Markup Language
- [Instructor] Sometimes we want to build models in one tool, such as Python, using interactive Jupyter notebooks, but then deploy that model using a different framework, such as a high-performance Java framework. For that, we have the Predictive Model Markup Language. The Predictive Model Markup Language, commonly known as PMML, is an XML standard for describing models. It's used to exchange models between data science and machine learning tools. A PMML model contains several sections, including a data dictionary, describing features used in the model, transformations, such as normalization and discretization, which map values into more useful forms, models, which is a description of the predictive model. The components in this section depend on the type of model. For example, a neural network model will list the type of activation function use in each node, and the number of layers in the network. Post-processing describes additional code that can be executed after the model is…
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