Machine learning teams are increasingly using Comet to automate the tracking of datasets and model-related information, which are termed "Artifacts," rather than manually recording them in tools like Excel. This process is crucial for maintaining clarity when conducting machine learning experiments, such as hackathons or organizational projects, where tracking datasets, model types, hyperparameters, and other critical details is essential. Comet simplifies this by allowing users to log Artifacts to either new or existing experiments on its platform, providing an efficient way to manage and retrieve datasets and other necessary files. Additionally, Comet enables the downloading of Artifacts from the platform to local machines, facilitating collaboration and ensuring team members have access to the necessary data files. This approach allows data scientists to focus more on enhancing model performance while ensuring effective experiment management.