Abstract: Disclosed is a method of integrally optimizing different types of parameters that require setting during a machine learning process. The disclosed method of integrally optimizing the parameters includes performing training on a machine learning model by selecting sensor parameters and machine learning model hyperparameters until a predetermined termination condition is satisfied; and determining, among the selected sensor parameters and machine learning model hyperparameters, an optimized sensor parameter and optimized machine learning model hyperparameter that minimize a loss value for the machine learning model, wherein the performing of the training on the machine learning model includes selecting the sensor parameters and machine learning model hyperparameters that satisfy a predetermined optimization range, and performing training on the machine learning model based on sensor data provided from a sensor by the selected sensor parameters and the machine learning model hyperparameters.