This section contains several examples of how to build models with Ludwig for a variety of tasks. For each task we show an example dataset and a sample model definition that can be used to train a model from that data.
Write-ups in the Tutorials section show both Ludwig's command line interface and Python API. For these tutorials, there are ready-to-run notebooks that work in Google's Colab Service. The notebooks provide the user a starting point for learning about Ludwig capabilities.
The Example Use Cases section illustrate how Ludwig can be applied to a variety of machine learning tasks, such as, natural language understanding, timeseries forcasting, multi-label classification to name just a few.
In addition to the examples here, on the Ludwig medium publication you can find a three part tutorial on Sentiment Analysis with Ludwig: