The aim of this report is to explore what types of data ML/AI developers work with. To begin, we broadly examine popular types of data and compare our findings between those involved in deep learning technologies and those who aren’t. In chapter two, we examine what influences their choices, while chapter three deep dives into whether data type and size influence the choice of architectures ML/AI developers use to train and deploy models. In the final chapter, we turn our attention to what ML/AI developers use each of these types of data for. Going further, we examine developers working on applications of the two most popular types of data — text and image data — and consider who their primary target audience is and what their main aims for involvement in the sector are.