The managed MLflow integration with Databricks on Google Cloud requires Databricks Runtime for Machine Learning 8.1 or above.
You can pull aggregate metrics on your MLflow runs using the mlflow.search_runs API and display them in a dashboard. Regularly such reviewing metrics can provide insight into your progress and productivity. For example, you can track improvement of a goal metric like revenue or accuracy over time, across many runs and/or experiments.
This notebook demonstrates how to build the following custom dashboard using the mlflow.search_runs API:
You can either run the notebook on your own experiments or against autogenerated mock experiment data.