In this paper, we present a neural network-enabled data distribution aware sorting method, coined as NN-sort. Our approach explores the potential of developing deep learning techniques to speed up large-scale sort operations, enabling data distribution aware sorting as a deep learning service. Compared to traditional pairwise comparison-based sorting algorithms, which sort data elements by performing pairwise operations, NN-sort leverages the neural network model to learn the data distribution and uses it to map large-scale data elements into ordered ones. Our experiments demonstrate the significant advantage of using NN-sort. Measurements on both synthetic and real-world datasets show that NN-sort yields 2.18x to 10x performance improvement over traditional sorting algorithms.