Hello! Here are the answers to your questions: 

- Yes! See databricks managed mlflow tracing - enable production monitor or endpoint config to collect traces in a delta table

- We have example code for implementing async feedback collection

- Definitely. See a comprehensive summary of considerations here. You can drill down into specific pieces of your workflow that you want to speed up - using caching and other techniques. 

- This is something you can take up directly with your Account Team. If you do not have one, you can make the formal request through Databricks support. As you near the 100,000-trace limit in MLflow, new traces may be rejected if rolling deletion isn’t enabled, disrupting streaming and batch syncing. If rolling deletion is active, old traces are purged to allow new ones, reducing historical data retention. High trace volumes near the limit can slow performance or cause errors for both streaming and batch jobs. Scaling resources and enabling rolling deletion help minimize issues.