The deep learning hype is real, and the Python ecosystem makes it easier than ever to neural networks to everything from speech recognition to generating memes. But when picking a model architecture to apply to your work, you should consider more than just state of the art results from NeurIPS. The amount of time, money and data available to you are equally, if not more, important. This talk will cover some alternatives to deep learning, including regression, tree-based methods and distance based methods. More importantly, it will include a frank discussion of the pros and cons of different methods and when it makes sense to use each in practice.