System, Method, and Computer Program Product for Machine Learning Model Visualization and Optimized Deployment

A computer-implemented method, comprising:

• generating, with at least one processor, a first set of candidate code objects using a first machine learning model, wherein generating the first set of candidate code objects comprises:
• generating at least one first candidate code object of the first set of candidate code objects without referencing an earlier-generated candidate code object of the first set of candidate code objects;
• generating at least one second candidate code object of the first set of candidate code objects by debugging an earlier-generated candidate code object of the first set of candidate code objects that is non-functional;
• and
• generating at least one third candidate code object of the first set of candidate code objects by improving an earlier-generated candidate code object of the first set of candidate code objects that is functional;
• determining, with at least one processor, for each candidate code object of the first set of candidate code objects, at least one value of at least one performance parameter;
• and
• deploying, with at least one processor, a selected code object of the first set of candidate code objects in a production environment based at least partly on the selected code object having an optimized value of the at least one performance parameter.

A computer program product comprising at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to:

• generate a first set of candidate code objects using a first machine learning model, wherein the program instructions that cause the at least one processor to generate the first set of candidate code objects cause the at least one processor to:
• generate at least one first candidate code object of the first set of candidate code objects without referencing an earlier-generated candidate code object of the first set of candidate code objects;
• generate at least one second candidate code object of the first set of candidate code objects by debugging an earlier-generated candidate code object of the first set of candidate code objects that is non-functional;
• and
• generate at least one third candidate code object of the first set of candidate code objects by improving an earlier-generated candidate code object of the first set of candidate code objects that is functional;
• determine, for each candidate code object of the first set of candidate code objects, at least one value of at least one performance parameter;
• and
• deploy a selected code object of the first set of candidate code objects in a production environment based at least partly on the selected code object having an optimized value of the at least one performance parameter.