With the advancement of the Industrial Internet of Things (IoT), the rapidly growing demand for data collection and processing poses a huge challenge to the design of data transmission and computation resources in the industrial scenario. Taking advantage of improved model accuracy by machine learning algorithms, we investigate the inner relationship of system performance and data transmission and computation resources, and then analyze the impacts of bandwidth allocation and computation resources on the accuracy of the system model in this paper. A joint bandwidth allocation and computation resource configuration scheme is proposed and the Karush-Kuhn-Tucker (KKT) conditions are used to get an optimal bandwidth allocation and computation configuration decision, which can minimize the total computation resource requirement and ensure the system accuracy meets the industrial requirements. Simulation results show that the proposed bandwidth allocation and computation resource configuration scheme can reduce the computing resource usage by 10% when compared to the average allocation strategy.