What does PMML serve as a bridge between?

PMML, or Predictive Model Markup Language, serves as a bridge between model development and implementation. It is an XML-based standard that allows different data mining and predictive analytics tools to share models seamlessly. When a predictive model is developed using a specific tool, PMML enables that model to be exported and imported into other systems without needing to rewrite it or adjust for different technical environments. This ensures that the model remains consistent and can be implemented in production environments effectively across various platforms.

This capability is crucial because it helps organizations maintain the integrity of their predictive models while leveraging diverse software tools for their data science workflows. The ability to transition smoothly from development to implementation facilitates faster deployment and broader usage of analytical models within business processes, driving more timely and informed decision-making.

In contrast, the other choices do not capture the specific role PMML plays in the data science lifecycle. Data collection and reporting, data cleansing and modeling, and qualitative versus quantitative analysis represent different stages or components of data analytics but do not specifically relate to the transfer and application of predictive models from their development phase into operational settings, which is the fundamental purpose of PMML.