Preparing training data for machine learning can be a bit of mystical science. One part data, one part alchemy,mand a very large helping of trial and error. In this session we will explore different methods of capturing and labeling data for use in machine learning models and artificial intelligence.

We will dispel the age old myth of “I don’t have enough data to do machine learning”, show how to crowd source, and quickly validate training data, and how to build rapid UX workflows for processing and labeling your data for the best outcomes. Who would have thought experience design had a place in machine learning?