Re: Spark schema: Cannot handle an ARRAY without specified content type
When DSS trains the model, before applying the preparation script, it needs to load the original dataset as a Spark dataframe. It therefore needs to transform the schema of the dataset to a Spark schema, which requires content types for arrays.
Only then is the preparation script applied, and the meanings taken into account.
--> In the specific case of Spark MLLib, you need to make sure that the storage type in the dataset is set to string, in addition to setting the meaning.