Last week, we started with using mlflow within databricks. The bayesian models that we are using right now are the pymc3 models (https://docs.pymc.io/en/v3/index.html).

We could use the experiment feature of databricks/mlflow to save the models as an artifact and then load them upon predicting.

However it would also be good to use the models feature of databricks/mlflow. We could not find a way (and it seems not to be supported right now) to use this feature for pymc3. Anyone has an idea how we could still use this?

Thanks!