Alternatives for Loading an MLflow Model

The following figure shows different alternatives for loading an MLflow model from within a UDF:

See the differences, prerequisites, benefits and drawbacks compared in the following table:

	From the Local File System	Via MLflow REST API
Speed	Fastest option	Significantly slower
Supported Artifact Stores	Only BucketFS	Arbitrary, incl. BucketFS
Setting the MLflow Tracking URI	Not required	Required

When you cannot guarantee the model to be accessible in the local file system of the UDF, some utility functions will help you to automatically choose the fastest loading option. See the examples in the following sections for details.

MLflow Tracking URI

In all cases where the UDF may access the MLflow server, it needs to set the MLflow Tracking URI. This can be done by:

• Setting the environment variable MLFLOW_TRACKING_URI or

• Calling mlflow.set_tracking_uri() within the UDF implementation.

Depending on the environment your Exasol instance is running in, the MLflow Tracking URI might differ from the one you can use on your local machine. This applies in particular when running an Exasol DockerDB instance inside a virtual machine.