Abstract
Data science and machine learning are hugely popular topics at the moment, but those topics don’t do the actual work involved justice. Most of the time is not spent on tweaking the features of the model or pruning it to optimise for edge deployment. It's spent finding, assessing and them wrangling the data into something meaningful and useful that the algorithm can get to work on. In this talk we will explore some of the challenges facing the data hunters and look to some methods that can help speed the process along and allow for repeatable data operations.