RealTimePredictor reference in sample notebooks

URL : https://github.com/aws/amazon-sagemaker-examples/blob/c4491228ce9f8eebcb3d411fd623d9dfd24f30b7/sagemaker-python-sdk/scikit_learn_inference_pipeline/Inference%20Pipeline%20with%20Scikit-learn%20and%20Linear%20Learner.ipynb

Below code snippet is throwing deprecation exception and unable to load container_type
from sagemaker.predictor import json_serializer, csv_serializer, json_deserializer, RealTimePredictor
from sagemaker.content_types import CONTENT_TYPE_CSV, CONTENT_TYPE_JSON
payload = 'M, 0.44, 0.365, 0.125, 0.516, 0.2155, 0.114, 0.155'
actual_rings = 10
predictor = RealTimePredictor(
endpoint=endpoint_name,
sagemaker_session=sagemaker_session,
serializer=csv_serializer,
content_type=CONTENT_TYPE_CSV,
accept=CONTENT_TYPE_JSON)

print(predictor.predict(payload))

https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker_model_monitor/enable_model_monitor/SageMaker-Enable-Model-Monitor.ipynb

https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker_model_monitor/enable_model_monitor/SageMaker-Enable-Model-Monitor.ipynb

Start with the linked “Inference Pipeline with Scikit-learn and Linear Learner” notebook and inspect its RealTimePredictor import and construction. Check the SageMaker Python SDK’s current predictor API and the referenced model-monitor notebook for related usage. Done means the sample notebook no longer raises the deprecation exception and its prediction call runs successfully.

Written by the indexing model from the issue text.