We are applying a groupby operation to a pyspark.sql.Dataframe and then on each group train a single model for mlflow. We see intermittent failures because the MLFlow server replies with a 429, because of too many requests/s

What are the best practices in those cases, and how do you limit the outgoing invocations of an external service? We are using managed MLFlow in Databricks, is there a way that we can configure mlflow so that it queues subsequent requests before sending them to the server?