Uber's Ludwig Gets a Second Version to Help You Build Machine Learning Models Without Writing Code

In the last couple of years, Uber has quietly become one of the most active contributors to open source machine learning technologies. From training frameworks like Horovod, statistical languages like Pyro or conversational stacks like the Plato Research Dialogue System, Uber has been pushing boundaries of innovation in the machine learning space with practical technologies rather than exoteric research. One Uber's most popular contributions to the machine learning ecosystem has been Ludwig, a framework for training and testing machine learning models without the need to write code. Recently, Uber released a second version of Ludwig that includes major enhancements in order to enable mainstream no-code experiences for machine learning developers. The goal of Ludwig is to simplify the processes of training and testing machine learning models using a declarative, no-code experience.