In this paper, we propose AuTraScale, an automated and transfer learning auto-scaling solution, to determine the appropriate parallelism and resource allocation that meet the latency and throughput targets. AuTraScale uses Bayesian optimization to adapt to the complex relationship between resources and QoS, minimizing the impact of resource interference on the prediction accuracy, and a new metric that measures the performance of operators for accurate optimization. Even when the input data rate changes, it can quickly adjust the parallelism of each operator in response, with a transfer learning algorithm.