Experiment tracking is a crucial aspect of machine learning (ML) that involves systematically recording and managing all relevant information from ML 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 comparison of different model iterations, ultimately leading to improved model performance and more efficient workflows. Tools like MLflow are commonly used to streamline this process, making it easier to visualize and analyze experimental outcomes.