With the advent of the Internet of Things (IoT) in manufacturing applications, current research is aimed at utilization of IoT data for process control. This article presents a novel approach to performing automated process feedback using a cloud-based IoT architecture. Specifically, a case study of automated spindle speed adjustment to enhance tool life in a machining operation is used to evaluate the proposed architecture. A data-driven model of tool flank wear evolution in a longitudinal turning operation is created on a cloud-based platform through measurements of a polyvinylidene fluoride thin film sensor voltage data and machine tool parameters monitored via MTConnect. The data are used to develop a Gaussian process regression (GPR) model to predict the average tool flank wear as a function of the measured quantities, which is then used to predict the remaining tool life. The performance of the GPR model is evaluated using 10-fold cross-validation and is shown to be sufficiently accurate for predicting the average flank wear with the coefficient of determination (R²) and the root mean square error values of 0.96 and 13.45 μm, respectively. A web application running the GPR model on the cloud platform is used to forecast the remaining tool life during a turning operation and when the predicted remaining tool life is less than desired, the web application commands a spindle speed override to automatically extend tool life. The architecture is demonstrated through longitudinal turning experiments on stainless steel 316L to extend tool life by 82 %. In addition, the latency of the architecture is evaluated and shown to be acceptable for the tool life enhancement application considered in this study.