International Business Machines Corporation
OPTIMIZATION FOR OPEN FEATURE LIBRARY MANAGEMENT

Abstract:

In an approach for optimizing an open feature library, a processor identifies redundancy among a set of features, the set of features previously executed in a machine learning model. A processor performs a predecessor evaluation, the predecessor evaluation including recognizing a feature in the set of features being executed and analyzing an impact of making an upstream feature configuration change relating to the feature. A processor performs a successor evaluation, the successor evaluation including recognizing the feature in the set of features being executed and analyzing an impact of making a downstream feature configuration change relating to the feature. A processor rates the feature against a goal, the goal including overall execution time and overall execution footprint in the machine learning model. A processor updates a state of the feature based on the predecessor evaluation, the successor evaluation, and the feature rating.