Experiment tracking is a vital process in machine learning that involves systematically recording all relevant information from experiments. This includes details such as source code, environment settings, data used, model configurations, hyperparameters, and performance metrics. By maintaining comprehensive records, experiment tracking ensures reproducibility, allowing researchers and practitioners to recreate experiments and verify results. It also facilitates better monitoring and management of machine learning workflows, ultimately leading to improved model performance and efficiency. Tools like MLflow are commonly used to streamline this process, making it easier to visualize and compare different experiment runs.