The first thing to try is to log in batches. If you are logging each param and metric separately, you're making 1 API call per param and 1 per metric. Instead, you should use the batch logging APIs; e.g. use "log_params" instead of "log_param" https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.log_params
If you're logging 10 params and metrics per model, this will cut the number of API calls you're making by a factor of 10.
If this optimization is still insufficient for you, then I'd recommend doing 2 things:
1) Short-term workaround: You can save data you wish to log to a table and log it in a follow-up process later.
2) Medium/long-term: I'd recommend working with your Databricks account team to come up with a solution to match your needs. In general, the best option is to reorganize how models are being fit or logged to do more efficient batching in logging. But different cases may have different best solutions, so working with your account team may be the best option.
