Noel

From the docs it looks like you want `LightFM.fit_partial`. http://lyst.github.io/lightfm/docs/lightfm.html#lightfm.LightFM.fit_partial

You could train your model offline using your full set of historical data, and afterwards update your model using the `LightFM.fit_partial` with the new data during production. In this case...

@fischjer4 I'm sorry but I just ran my example without any error (`lightfm==1.15`). If you run my example and still get an error can you try and increase the number...

I'm happy you sorted out the issue.

@ynorouzz Please check out the exchange between @alexmilano and @JohnPaton. You're getting this error because you've added new feature columns. `fit_partial` will update existing columns but not add new ones.

@RohitMungi-MSFT Thank you for your response. CLI Auth doesn't look like it meets my needs. Using a Service Principal would work but it requires: 1) A Service Principal. 2) A...

@rastala Thank you. That is what I expected but while in a `DatabricksStep` it doesn't seem to simply work. `Run.get_context(allow_offline=False)` produces: ``` KeyError Traceback (most recent call last) /databricks/python/lib/python3.7/site-packages/azureml/core/run.py in...

Just a friendly reminder that this ticket still exists.

@fahdkmsft It's unclear that this is the intended usage given that the only documentation related to these additional variables is under [DatabricksStep -> python_script_name](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py#parameters) and makes no mention of setting...