At high redshift, due to both observational limitations and the variety of galaxy morphologies in the early universe, measuring galaxy structure can be challenging. Non-parametric measurements such as the CAS system have thus become an important tool due to both their model-independent nature and their utility as a straightforward computational process. With multiple ‘Big Data’ surveys planned in the near future, it will become computationally infeasible to use current algorithms to compute these parameters. One solution to this problem is to use machine learning. In this talk we present how deep learning, specifically Convolutional Neural Networks (CNNs) can be utilised to reproduce these parameters at a much faster rate. Once trained, measurements with our networks are >103 times faster than previous methods. We will also discuss how Bayesian Optimisation can be applied to select suitable network architectures for this problem. This approach shows the potential of employing neural networks to future surveys to provide superior results in substantially less time.