A better way to build ML -- why you should be using Active Learning

Data labelling is often the biggest bottleneck in machine learning -- finding, managing and labelling vast quantities of data to build a sufficiently performing model can take weeks or months. Active learning lets you train machine learning models with much less labelled data. We think you should too. Imagine that you wanted to build a spam filter for your emails. The conventional approach (at least since 2002) is to collect a large number of emails, label them as "spam" or "not spam" and then train a machine learning classifier to distinguish between the two classes.