Hi,
I have created a an ML model in Azure ML using auto ML. This has been deployed as an endpoint using the UI.
This is a sample of the original dataset: 
Its deployed as a container instance and the deployment state is healthy.
When I test the endpoint, it pre-populates the test form with some example values, for the various paratmeters which are strings and ints.

However, if I populate the blank fields with ints and leave the strings, .

then test the service, I get an error that suggests it is trying to convert attribute13 to an int

as you can see, it is fine with the productName parameter being a string, but tries to convert Attribute13 to an int.
The same happens with the other attributesXX.
If I set all the attributes to numerical values, the test completes and the endpoint returns a value from the model as expected.
IF i check the swagger file, it shows that the api is expecting a string:

So that all suggests the issue exists somewhere in the python code created automatically.
This is kinda where I get stuck - I see resources on debugging the python code, I can see in my score.py file the example sample passes specifies these as 'object' dtypes: 
And after that I dont know where to go from here - feels like it should just work 'out of the box' as I got to this point purely through the UI.
Any help greatly appreciated.
Steve

