Comet.ml is a platform designed to streamline the machine learning workflow by automatically tracking code, experiments, hyperparameters, and results to enhance reproducibility and transparency. It addresses challenges faced by data scientists dealing with disjointed scripts and complex file structures, offering native support for various popular machine learning frameworks like TensorFlow, Keras, and PyTorch. The core feature is the Experiment class, which logs script output, code, hyperparameters, metrics, and model configurations, while offering methods to set experiment names, hash datasets, and log assets, metrics, parameters, and figures. Users can conduct meta-analysis across experiments with project-level visualizations and code diffs, facilitating efficient iteration cycles. Founded by Gideon Mendels, Comet.ml provides a comprehensive MLOps solution to accelerate the deployment of machine learning models into production.