Given a scenario where you have a TensorFlow machine learning model executing on Compute Engine virtual machines (n2-standard-32), it currently takes two days to complete the training process. The model includes custom TensorFlow operations that require partial execution on a CPU. Your objective is to cut down the training duration while maintaining cost efficiency. How should you proceed?