Real-Time AI Model Performance Alerts via SMS

Question

Pulumi · Accepted Answer

To set up real-time AI model performance alerts via SMS using Pulumi, we'll use a combination of cloud services and Pulumi resources. We'll take a hypothetical AI model performance monitoring service that triggers an alert whenever a certain condition is met, such as the performance dipping below a threshold. Once an alert is triggered, we want to send an SMS to a specified phone number.

For this scenario, you might imagine we have a monitoring system already in place (perhaps using AWS CloudWatch, Google Cloud Monitoring, or another monitoring service), and the particular details of that setup will vary depending on the cloud provider and services you're using.

Given that, here's a general outline of how we can achieve this using AWS as an example:

Amazon Pinpoint: We'll use Amazon Pinpoint to send SMS messages. We'll need to set up an SMS channel, which enables us to send messages to users' phones.
AWS Lambda: We'll create a Lambda function that will be triggered in response to the model performance alert. This function will send the SMS via Amazon Pinpoint.
Amazon SNS or CloudWatch Event: This will be the trigger mechanism for our Lambda function. We can configure an SNS topic or a CloudWatch Event based on the performance metrics of our AI model. The Lambda function will be set as the target for this trigger.

For the purpose of this tutorial, I'll give you a program that sets up an AWS Lambda function and configure Amazon Pinpoint to send an SMS. Please note that the details of setting up the actual metric monitoring are not covered here; instead, you'll need to connect your monitoring solution to trigger the Lambda.

Here's a Pulumi program that creates a Lambda function and sets up Amazon Pinpoint to send an SMS:

import json
import pulumi
import pulumi_aws as aws

# Create an IAM role for the Lambda function
lambda_role = aws.iam.Role("lambdaRole",
    assume_role_policy=json.dumps({
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            }
        }]
    }))

# Attach the AWSLambdaBasicExecutionRole policy to the role
role_policy_attachment = aws.iam.RolePolicyAttachment("lambdaRoleAttachment",
    role=lambda_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole")

# Set up the Amazon Pinpoint project/application
pinpoint_app = aws.pinpoint.App("pinpointApp")

# Create an SMS channel for the Pinpoint application
sms_channel = aws.pinpoint.SmsChannel("smsChannel",
    application_id=pinpoint_app.application_id,
    enabled=True)

# Write the Lambda function code that will use Pinpoint to send the SMS
# For this example, we're hardcoding the phone number and message.
# In a real-world scenario, you'd dynamically set these based on the alert.
lambda_function_code = """
import json
import boto3

def lambda_handler(event, context):
    pinpoint_client = boto3.client('pinpoint')
    response = pinpoint_client.send_messages(
        ApplicationId='{application_id}',
        MessageRequest={{
            'Addresses': {{
                '{phone_number}': {{
                    'ChannelType': 'SMS'
                }}
            }},
            'MessageConfiguration': {{
                'SMSMessage': {{
                    'Body': 'Alert: Your AI Model performance is below the threshold.',
                    'MessageType': 'TRANSACTIONAL'
                }}
            }}
        }}
    )
    return response
""".format(application_id=pinpoint_app.application_id, phone_number='<YOUR_PHONE_NUMBER>')

# Create the Lambda function
lambda_function = aws.lambda_.Function("modelPerformanceAlertFunction",
    role=lambda_role.arn,
    handler="index.handler",
    runtime="python3.8",
    code=pulumi.Output.all(lambda_function_code).apply(lambda args: pulumi.AssetArchive({
        "index.py": pulumi.StringAsset(args[0])
    })),
    opts=pulumi.ResourceOptions(depends_on=[role_policy_attachment]))

# Export the Lambda function name and the Pinpoint application ID
pulumi.export('lambda_function_name', lambda_function.name)
pulumi.export('pinpoint_application_id', pinpoint_app.application_id)

This Python program will set up a basic alerting system that sends an SMS message when triggered. The Lambda role and the function are created with necessary permissions, and the inline Python code for the Lambda function includes the logic to send SMS messages using Amazon Pinpoint.

Remember to replace <YOUR_PHONE_NUMBER> with the actual number you want to send alerts to.

To implement this system fully, you would need to set up an SNS topic or a CloudWatch Event that triggers this Lambda function based on your AI model's performance metrics.

This program sets up only the AWS infrastructure needed to send an SMS alert. Connecting it to your AI model monitoring or another alerting mechanism would depend on your specific use case and would entail additional resources such as setting up CloudWatch alarms or SNS topics and subscriptions.