Systems and methods for rule-based machine learning model promotion

A system for rule-based promotion of a machine learning model, comprising:

• at least computer including a processor, wherein the processor is configured to:
• a rules engine executing on the at least one computer, wherein the rules engine defines a software object model, and an evaluation framework, wherein the rules engine checks adherence of the model rule file and the model metadata file to a schema of the software object;
• a model metadata file, wherein a format of the model metadata file is based on the software object model, wherein the model metadata file stores metadata associated with the machine learning model, wherein the metadata includes a parameter value, and wherein the parameter value describes a characteristic of the machine learning model;
• a model rule file, wherein a format of the model rule file is based on the software object model, and wherein the model rule file defines rule criteria for evaluating the metadata;
• and
• wherein the rules engine is configured to:
• instantiate a software object based on the software object model;
• parse the model rule file to determine the rule criteria;
• parse the model metadata file to determine that the parameter value is associated with the rule criteria;
• set the value of an operand declared in the software object to the parameter value;
• perform an evaluation according to a hierarchy of the rule criteria that begins at a root node such that the operand defined at the root node must evaluate before evaluating a next level of the hierarchy, the evaluation comprising evaluating the operand against an operator indicated by the rule criteria;
• and
• provide a promotion decision of the model based on the evaluation.