Machine learning model development and optimization process that ensures performance validation and data sufficiency for regulatory approval

A method, comprising:

• training, by a system operatively coupled to a processor, a machine learning model to perform an inferencing task on an initial set of data samples included in a sample population;
• determining, by the system, subgroup performance measures and uncertainty for subgroups of the data samples respectively associated with different metadata factors, wherein the subgroup performance measures reflect performance accuracy and uncertainty of the machine learning model with respect to the subgroups;
• and
• determining, by the system, whether the machine learning model meets an acceptable level of performance for deployment in a field environment based on whether the subgroup performance measures respectively satisfy a threshold subgroup performance measure.

A machine-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:

• training a machine learning model to perform an inferencing task on an initial set of data samples included in a sample population;
• determining subgroup performance measures for subgroups of the data samples respectively associated with different metadata factors, wherein the subgroup performance measures reflect performance accuracy of the machine learning model with respect to the subgroups;
• and
• determining whether the machine learning model meets an acceptable level of performance for deployment in a field environment based on whether the subgroup performance measures respectively satisfy a threshold subgroup performance measure.