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SYSTEMS AND METHODS FOR IMPLEMENTING DATA TRANSFORMATIONS IN MULTIPLE EXECUTION CONTEXTS

Abstract:

A data transformation system for implementing reproducible and consistent data transformations in multiple execution contexts (batch, streaming, etc.) where the transformation function/logic initially acts on historical raw data to produce derived data to train a machine learning model. When the model is trained and deployed to handle streaming event data, the same transformation is reused to transform streaming data into the appropriate derived data for the model scoring, and later for a refit of the model.