A deep learning algorithm is trained to predict wall-shear stress based on flow in the outer region of transitional and turbulent boundary layer flow. Flow variables sampled from the boundary layer region of eight high quality wall-resolved Large Eddy Simulations of transonic compressor cascades in the presence of shock-boundary layer interaction effects are used to train the model. About 1.1 TB of data stored in compressed text format generated from the LES calculations is used to perform the model training. The model is shown to be generic and able to predict complex boundary layer physics including laminar to turbulent transition caused by the shock-boundary layer interaction.