Ludwig is a TensorFlow-based toolbox designed to enable users to train and test deep learning models without writing code, facilitating a seamless machine learning pipeline from data preprocessing to model training and visualization. By integrating Ludwig with comet.ml, users can track their experiments in a reproducible manner, enhancing visibility and understanding of the research process. The article provides a step-by-step guide on setting up and using Ludwig for a text classification task using the Reuters-21578 dataset, highlighting the use of CLI commands and the integration with comet.ml for live experiment tracking. Users are guided through the installation of necessary software, dataset preparation, model definition, and execution of experiments with the comet flag, which allows for the capture and visualization of experiment data.