Hi all,

I have been using databricks-connect with serverless compute to develop and debug my databricks related code. It worked great so far. Now I started integrating ML-Flow in my workflow, and I am encountering an issue. When I run the following code, I get an exception out of the spark runtime.

import mlflow
import databricks.connect as db_connect

mlflow.login(). # This prints an INFO-log: Login successfull!
# mlflow.set_model_uri("databricks)
spark_ctx = db_connect.DatbricksSession.builder.serverless(True).getOrCreate()
train_and_log_ml_model(spark_ctx)

 The error message is the following:

pyspark.errors.exceptions.connect.AnalysisException: [CONFIG_NOT_AVAILABLE] Configuration spark.mlflow.modelRegistryUri is not available. SQLSTATE: 42K0I

What am I missing? I there a way, to make it work?

Greetings, Daniel

P.S.: My environment is quite bare-bones: A new python-venv, where I pip installed `databricks-connect==15.1` and `mlflow`. I have configured the databricks-cli to use SSO, with a DEFAULT profile in the file `~/.databrickscfg`.