Getting started with any machine learning project often starts with the question: “How much data is enough?”. The response depends on a number of factors like the diversity of production data, the availability of open-source datasets, the expected performance of the system, and the list can go on for quite a while. In this article, I’d like to debunk a popular myth about machines only learning from large amounts of data, and share a use case of applying ML with a small dataset.