Accessing Artifacts from Within a UDF

Using the Exasol MLflow Plugin significantly speeds up loading MLflow models in Exasol UDFs.

After having built, deployed, and activated your SLC, you can use Exasol SQL to define a UDF like this:

--/
CREATE OR REPLACE MLFLOW_SLC
   SCALAR SCRIPT "<SCHEMA>"."<UDF_NAME>"(uri VARCHAR(2000))
   RETURNS BOOL AS
import mlflow
from exasol.mlflow_plugin.artifacts.bucketfs_connector import udf_path
def run(ctx):
    path = udf_path(ctx.uri)
    model = mlflow.sklearn.load_model(path)
    #--
    #-- your implementation using the model goes here
    #--
    return True
/

Now you can run the UDF via the following SQL statement

SELECT "<SCHEMA>"."<UDF_NAME>"('exa+bfs://...');