Azure Databricks provides a managed version of the MLflow
tracking server, which hosts the MLflow REST API. You can invoke the MLflow REST API using URLs of the form
<databricks-instance> with the
<region>.azuredatabricks.net domain name of your Azure Databricks deployment.
The MLflow APIs are rate limited as four groups, based on their function and maximum throughput. The following is the list of API groups and their respective limits in qps (queries per second):
- Low throughput experiment management (list, update, delete, restore): 7 qps
- Search runs: 7 qps
- Log batch: 47 qps
- All other APIs: 127 qps
If the rate limit is reached, subsequent API calls will return status code 429. All MLflow clients (including the UI) automatically retry 429s with an exponential backoff.