Systems and methods for optimization of a data model network architecture for target deployment

A non-transitory medium holding computer-executable instructions for selecting an optimized data model architecture, the instructions when executed causing at least one computing device to:

• receive one or more resource constraints for a target deployment;
• generate a plurality of random model architectures from a set of model architecture production rules subject to the one or more resource constraints, each random model architecture defined by randomly chosen values for one or more meta parameters and one or more layer parameters;
• adaptively refine one or more of the plurality of random model architectures to improve performance relative to a metric;
• and
• select the refined model architecture with the best performance relative to the metric.

A system for selecting an optimized data model architecture, comprising:

• a computing device including a processor and a memory operatively coupled to the processor, the memory having instructions stored therein that when executed by the processor cause the computing device to:
• receive one or more resource constraints for target deployment;
• generate a plurality of random model architectures using a random model architecture generation module from a set of model architecture production rules subject to the one or more resource constraints, each random model architecture defined by randomly chosen values for one or more meta parameters and one or more layer parameters;
• adaptively refine one or more of the plurality of random model architectures using an adaptive refinement module to improve performance relative to a metric;
• and
• select the refined model architecture with the best performance relative to the metric.