A method of automating model creation, the method comprising: generating, by one or more processors and based on simulation configuration files, tester configuration objects for performing a plurality of simulation iterations with respect to a network device operating within a test environment, each of the simulation iterations configured to randomly assign parameters within the tester configuration objects to reflect actual network environments; conducting, by the one or more processors and based on configuration objects, the configuration objects comprising the tester configuration objects and device under test, DUT, configuration objects, each of the plurality of simulation iterations within the test environment to collect a corresponding plurality of simulation datasets representative of operating states of the network device, wherein each of the plurality of simulation datasets include feature usage data indicative of operating states of the network device relative to the test environment, and resource utilization data indicative of resource utilization within the network device, wherein the method further comprises identifying associations between the feature usage data of each of the plurality of simulation datasets and the corresponding resource utilization data of each of the plurality of simulation datasets; and performing, by the one or more processors, machine learning with respect to each of the plurality of simulation datasets to generate a model representative of the network device that predicts, responsive to configuration parameters for the network device, an operating state of the network device when configured with the configuration parameters for the network device, wherein the configuration parameters correspond to an instance of the DUT configuration objects, and wherein performing the machine learning comprises performing machine learning with respect to the associations between the feature usage data of each of the plurality of simulation datasets and the corresponding resource utilization data of each of the plurality of simulation datasets. generating, by one or more processors and based on simulation configuration files, tester configuration objects for performing a plurality of simulation iterations with respect to a network device operating within a test environment, each of the simulation iterations configured to randomly assign parameters within the tester configuration objects to reflect actual network environments; conducting, by the one or more processors and based on configuration objects, the configuration objects comprising the tester configuration objects and device under test, DUT, configuration objects, each of the plurality of simulation iterations within the test environment to collect a corresponding plurality of simulation datasets representative of operating states of the network device, wherein each of the plurality of simulation datasets include feature usage data indicative of operating states of the network device relative to the test environment, and resource utilization data indicative of resource utilization within the network device, wherein the method further comprises identifying associations between the feature usage data of each of the plurality of simulation datasets and the corresponding resource utilization data of each of the plurality of simulation datasets; and performing, by the one or more processors, machine learning with respect to each of the plurality of simulation datasets to generate a model representative of the network device that predicts, responsive to configuration parameters for the network device, an operating state of the network device when configured with the configuration parameters for the network device, wherein the configuration parameters correspond to an instance of the DUT configuration objects, and wherein performing the machine learning comprises performing machine learning with respect to the associations between the feature usage data of each of the plurality of simulation datasets and the corresponding resource utilization data of each of the plurality of simulation datasets.