Multi-modal deep learning in less than 15 lines of code - KDnuggets

For many machine learning use-cases, organizations rely solely on tabular data and tree-based models like XGBoost and LightGBM. This is because deep learning is simply too hard for most ML teams. As a result, teams miss out on valuable signals hidden within unstructured data like text and images. New declarative machine learning systems--like open-source Ludwig started at Uber--provide a low-code approach to automating ML that enables data teams to build and deploy state-of-the-art models faster with a simple configuration file. Specifically, Predibase--the leading low-code declarative ML platform--along Ludwig make it easy to build multi-modal deep learning models in 15 lines of code.