1. An apparatus comprising: circuitry communicatively coupled to a data center; a distributor for execution by the circuitry to receive an indication of a workload distribution for a workload to be supported by one or more computing resources of a data center; a classify component for execution by the circuitry to determine a class for the workload based on a workload type or profile for the workload; a modeler for execution by the circuitry to determine predicted operating values for at least one of the one or more computing resources based on the class and input the one or more predicted operating values in at least one scoring model to evaluate the workload being supported by the at least one of the one or more computing resources; a scheduler for execution by the circuitry to schedule the workload to the at least one of the one or more computing resources based on the evaluation; a collector for execution by the circuitry to gather training operating information for one or more workloads included in the workload type or profile while the one or more computing resources of the data center support the one or more workloads, the training operating information including an inlet or an outlet temperature for a platform or rack housing the one or more computing resources, a power consumption for a platform or rack housing the one or more computing resources, processor cache miss information, network data throughput latency information, memory access latency information, throttling activation information for the one or more computing resources, margin to a peak operating temperature threshold for the one or more computing resources, or a volumetric airflow for a platform and/or rack housing the one or more computing resources; and a store for execution by the circuitry to store the gathered training operating information, the classify component to use the stored and gathered training operating information to: cluster group the training operating information via k-means clustering; learn a classifier based on the cluster group of the training operating information via a support vector machine (SVM); and assign a regression fit function to classified training operating information classified by the classifier based on a least squares approach.