Data accepted by the endpoint should be accepted and processed into a format(s) that the model will ingest.
The processor.py file accepts CSV data and converts it to a pandas dataframe.
The model only accepts a single nested numpy array.
Therefore if you try to give the sagemaker container a numpy array it will throw an error due to it only accepting csv data and if you provide csv data the model will error due to only accepting a nested array.
Additionally, the method for inferencing is currently giving an error due to the import being directly tied to a tensorflow based model.
Reconfigure processor.py so that it accepts csv data but converts that data into a nested numpy array.
Additionally we must make use of Sagemaker's SDK Predictors class.
- Deploy endpoint
- Using the fulldeploy notebook, run all cells
- Observe type error from accepting a csv
This is a core functionality blocker.