Thanks for your response. It sounds like the 2nd approach is best for me, modifying the `predict()` method to perform the required history lookup.

Is it possible to do this via the Feature Engineering client within that method, or should I simply query the Unity Catalog directly? I.e. something like:

query = """"
SELECT date, widget_sales
FROM my_catalog.my_schema.widget_sales
WHERE date < as_of_date
"""
sdf_widget_sales_history = spark.sql(query)
[...]

Which seems straightforward enough, to be fair. Though I guess in this case, I take it I shouldn't log the model using the Feature Engineering client, and instead just log via MLFlow directly? As the client seems to want either a TrainingSet object or FeatureSpec, which wouldn't be applicable here, as I'm DIY'ing the history lookup?