We'll introduce Bayesian optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time consuming or expensive. Deep learning pipelines are notoriously expensive to train and often have many tunable parameters, including hyperparameters, the architecture, and feature transformations, that can have a large impact on the efficacy of the model. We'll provide several example applications using multiple open source deep learning frameworks and open datasets. We'll compare the results of Bayesian optimization to standard techniques like grid search, random search, and expert tuning. Additionally, we'll present a robust benchmark suite for comparing these methods in general.