Is Redshift mixing up my data columns when creating a model?

Hello,

I'm using running:

create model predict_xxxxx
from (select col1, col2, col3 from my_table)
target col3
function predict_xxx
iam_role 'arn:aws:iam::xxxxxxx:role/RedshiftML'
problem_type regression
objective 'mse'
settings (
    s3_bucket 'redshiftml-xxxxxxx',
    s3_garbage_collect off,
    max_runtime 1800
);

Which then generates input data files in CSV format in the S3 bucket I specified, but when I open up those files and look at them, all the columns in my select statement are present, but the column headers are mismatched with the data below them. I see col1 data under the col2 column and so on. I know the data is mixed up because the data types and numeric ranges are different for each column. I double-checked my table and the columns and data are matched correctly. Is Redshift/Sagemaker then using that mismatched data to train the model? I have tried with only two column and it still gets mixed up. I've tried using a table instead of a select expression and the problem persists.

Any insight is appreciated.

Thanks,

• SV