Creating large training data sets quickly

In this episode of the Data Show, I spoke with Alex Ratner, a graduate student at Stanford and a member of Christopher Ré’s Hazy research group. Training data has always been important in building machine learning algorithms, and the rise of data-hungry deep learning models has heightened the need for labeled data sets. In fact, the challenge of creating training data is ongoing for many companies; specific applications change over time, and what were gold standard data sets may no longer apply to changing situations.