使用机器学习模型

import json
import boto3
import os

# Receives a device shadow Accepted document from IoT Core rules engine.
# Event has signature like {"state": {"reported": {"sound": 5}}}.
# See expectedAttributes for full list of attributes expected in state.reported.
# Builds CSV input to send to SageMaker endpoint, name of which stored in
#   environment variable SAGEMAKER_ENDPOINT.
#
# Returns the prediction and confidence score from the ML model endpoint.
def lambda_handler(event, context):
    client = boto3.client('sagemaker-runtime')
    
    print('event received: {}'.format(event))
    
    # Order of attributes must match order expected by ML model endpoint. E.g.
    #   the same order of columns used to train the model.
    expectedAttributes = ['sound', 'temperature', 'hvacStatus', 'roomOccupancy', 'timestamp']
    reported = event['state']['reported']
    reported['timestamp'] = event['timestamp']
    reportedAttributes = reported.keys()
    
    # Validates the input event has all the expected attributes.
    if(len(set(expectedAttributes) & set(reportedAttributes)) < len(expectedAttributes)):
        return {
            'statusCode': 400,
            'body': 'Error: missing attributes from event. Expected: {}. Received: {}.'.format(','.join(expectedAttributes), ','.join(reportedAttributes))
        }
    
    # Build the input CSV string to send to the ML model endpoint.
    reportedValues = []
    for attr in expectedAttributes:
        reportedValues.append(str(reported[attr]))
    input = ','.join(reportedValues)
    print('sending this input for inference: {}'.format(input))

    endpoint_name = os.environ['SAGEMAKER_ENDPOINT']
    content_type = "text/csv"
    accept = "application/json"
    payload = input
    response = client.invoke_endpoint(
        EndpointName=endpoint_name, 
        ContentType=content_type,
        Accept=accept,
        Body=payload
        )
        
    body = response['Body'].read()
        
    print('received this response from inference endpoint: {}'.format(body))
    
    return {
        'statusCode': 200,
        'body': json.loads(body)['predictions'][0]
    }

{
    "state": {
        "reported": {
            "sound": 20,
            "temperature": 58.8,
            "hvacStatus": "HEATING",
            "roomOccupancy": true
        }
    },
    "timestamp": 1234567890
}

{
  "statusCode": 200,
  "body": {
    "predicted_label": "false",
    "probability": "0.9999991655349731"
  }
}

SELECT cast(get(get(aws_lambda("arn\:aws\:lambda\:REGION\:ACCOUNT_ID\:function\:FUNCTION_NAME", *), "body"), "predicted_label") AS Boolean) AS state.desired.roomOccupancy FROM '$aws/things/<<CLIENT_ID>>/shadow/update/accepted' WHERE state.reported.sound <> Null