Worked on the mlflow/mlflow repository to deliver cross-flavor support for including extra files in model artifacts, addressing a key need for enhanced model management and deployment flexibility. Developed a utility in Python that enables users to specify additional files when saving or logging models, ensuring these files are copied into the model directory regardless of the model flavor. This approach improved reproducibility and deployment readiness by standardizing how extra files are handled across different MLflow flavors. The work focused on integrating the extra_files parameter into save_model and log_model functions, with comprehensive tests to validate consistent behavior across the MLflow platform.