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The change only fixes the Problem with nan values. As soon you have any text data with None value it breaks: This problem occurs as soon as there is an attempt to pass any non-numeric values. For instance, I have a df that contains text values, some of which can be None. A fix would be to check for float values in mlserver/codecs/numpy.py row 109: if isinstance(val, float) and np.isnan(val)
While trying to handle missing values, I've noticed that json serialization is not correct:
serialized request:
In case anyone else is facing the same issue, this is my quick-n-dirty way to handle it:
In case this is a simple fix that could be handled by a newcomer like me, I would be interested to work on the bug fix.
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