Drilling operations rely on learned expertise in monitoring the drilling performance data and the rock data to assess the dull condition of the drill bit. While human learning can subjectively pick up the indicators based on rig surface data streams, this information is highly convoluted with changes in rock and drilling data. Recent approaches for bit wear estimation also include model-based and traditional supervised machine learning methods, which are usually costly and time-consuming. In this study, we developed a bi-directional long short-term memory-based variational autoencoder (biLSTM-VAE) to project raw drilling data into a latent space in which the real-time bit-wear can be estimated. The proposed deep neural network was trained in an unsupervised manner, and the bit-wear estimation is demonstrated as an end-to-end process.