Ludwig, a TensorFlow-based toolbox, enables users to train and test deep learning models without writing code, offering a comprehensive codeless pipeline from start to finish. By integrating Ludwig with Comet.ml, users can track their experiments live, providing transparency and better understanding of the research process. Comet.ml complements Ludwig by allowing users to compare experiments, organize visualizations, and conduct meta-analysis, ultimately enhancing model development. The integration facilitates capturing experiment details, live performance metrics, and visualizations, offering an efficient way to manage machine learning workflows. The text provides a step-by-step guide to using Ludwig with Comet.ml for a text classification model, emphasizing the seamless execution and tracking of experiments and highlighting the synergy between the two tools for building and evaluating deep learning models.