A data scientist has computed a Python dictionary containing per-class precision and recall values that they want to persist with the current MLflow run as a JSON artifact. They want the cleanest approach that stores the dictionary directly as a named JSON file in the run's artifact store without manually writing a temporary file to local disk first. Which MLflow call should they use inside the active run?