Introducing Ludwig, a Code-Free Deep Learning Toolbox

We have been developing Ludwig internally at Uber over the past two years to streamline and simplify the use of deep learning models in applied projects, as they usually require comparisons among different architectures and fast iteration. We have witnessed its value to several of Uber's own projects, including our Customer Obsession Ticket Assistant (COTA), information extraction from driver licenses, identification of points of interest during conversations between driver-partners and riders, food delivery time prediction, and much more. For this reason we decided to release it as open source, as we believe there is no other solution currently available with the same ease of use and flexibility.