Implementing model monitoring for image dataset.

I tried to implement model monitoring but I do not understand how to implement it for Image dataset. Specially I am not sure how the 4 dimensional data for the image is to be handled for model monitoring. I have defined a preprocess_handler but not sure if that's the function which can truly help.

In my code I am generating false ground truth and invoke the model as follows:
```
def generate_load_and_ground_truth():
df = pd.read_csv('validation_with_predictions.csv')
gt_records = []
for i, row in df.iterrows():
suffix = uuid.uuid1().hex
inference_id = f'{i}-{suffix}'
data = np.array([X_test[i]])
payload = {'instances': data}
args = {'InferenceId': inference_id}
out = predictor.predict(data = payload, initial_args = args)
gt_records.append(str({
"groundTruthData": {
"data": str(df['label'][i]),
"encoding": 'CSV',
},
"eventMetadata": {
"eventId": str(inference_id),
},
"eventVersion": "0",
}))
upload_ground_truth(gt_records, ground_truth_upload_path, datetime.utcnow())

def upload_ground_truth(records, path, upload_time):
data_to_upload = ",".join(records)
data_to_upload = data_to_upload
target_s3_uri = f"{path}/{upload_time:%Y/%m/%d/%H/%M%S}.jsonl"
print(f"Uploading {len(records)} records to", target_s3_uri)
S3Uploader.upload_string_as_file_body(data_to_upload, target_s3_uri)
```

When the model schedule ran it gave the error message
```
'MonitoringExecutionStatus': 'Failed',
'FailureReason': 'Algorithm Error: See Job Logs for more information.'
Looking into the cloud watch log I found the error to be:
'Cannot resolve column name "groundTruthMetadata.eventId" among (_corrupt_record);'
```

Then I used this pre-process function at my endpoint.
```
import json
def preprocess_handler(inference_record):
input_dict = json.loads(inference_record.endpoint_input.data)
output_dict = json.loads(inference_record.endpoint_output.data)
input_data = str(input_dict['instances'].reshape(3072))[1:-1]
output_data = str(np.argmax(output_dict['predictions'][0]))
return_dict = {'prediction000':output_data, 'feature000':input_data}
return return_dict
```

It gives the error:
```
'FailureReason': 'InternalServerError: We encountered an internal error. Please try again.'}
```