call_mlflow_sagemker_endpoint.py

import boto3
import json

# Name of the app that you defined during the deployment on sagemaker
app_name = "xxxxx"
# AWS region of the deployment of the app on sagemaker
region = "xxxxx"

# Function to collect data from the endpoint on sagemaker
def query_endpoint(input_json):
  client = boto3.session.Session().client("sagemaker-runtime", region)

  response = client.invoke_endpoint(
      EndpointName=app_name,
      Body=input_json,
      ContentType='application/json; format=pandas-split',
  )
  preds = response['Body'].read().decode("ascii")
  preds = json.loads(preds)
  print("Received response: {}".format(preds))
  return preds

# Make a call to the endpoint
query_endpoint(input_json)