1. Automated Email Notifications on AI Model Training Completion with AWS SESv2


    To create automated email notifications on AI model training completion using AWS Simple Email Service (SES) version 2, we need to establish an AWS SES configuration that can send emails when triggered. The usual flow for such automation would include:

    1. Setting up an email identity in AWS SES to send emails from. This will involve verifying a domain or email address that will be used as the sender.
    2. Creating an AWS Lambda function or using another service to detect when the AI model training is complete. This might involve polling an AWS service like Amazon SageMaker or receiving a callback if the service supports it.
    3. The Lambda function, when triggered upon completion of model training, will then send an email using AWS SESv2 to notify the user.

    Let's walk through the Pulumi code that will set up the AWS SESv2 email identity and assume that you will have a separate Lambda function that triggers sending the email notification.

    Here's how you can set up your AWS SESv2 email identity using Pulumi and Python:

    import pulumi import pulumi_aws as aws # Configure AWS SES email identity (domain or email address) # Replace 'your-email@example.com' with the email you want to verify with SES email_identity = aws.sesv2.EmailIdentity("MyEmailIdentity", email_address="your-email@example.com") # Output the email identity ARN pulumi.export("email_identity_arn", email_identity.arn)

    The above program creates an email verification request in AWS SESv2 for the specified email address. After running this Pulumi code, you'll need to check the inbox of the email you're verifying and follow the instructions AWS sends to confirm that you own the email address. Once verified, you can use this email identity to send notifications.

    Now, to send an email when your AI model training is complete, you will need some additional AWS resources not covered in the search results, like an AWS Lambda function that detects the completion event and then uses AWS SES to send the email. The completion event can be an S3 event, an AWS Step Function state change, or a notification from Amazon SageMaker.

    The code snippet below is an example of how you might use AWS Lambda with Pulumi to respond to a completion event. Note that you will need to fill in your logic for when and how the event is triggered:

    import json import pulumi_aws as aws # Define the IAM role and policy that allows Lambda to send emails using SES lambda_role = aws.iam.Role("lambdaRole", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Principal": { "Service": "lambda.amazonaws.com", }, "Effect": "Allow", "Sid": "", }] })) lambda_policy = aws.iam.RolePolicy("lambdaPolicy", role=lambda_role.id, policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": [ "ses:SendEmail", "ses:SendRawEmail" ], "Resource": "*", }] })) # Define the Lambda function lambda_function = aws.lambda_.Function("myLambdaFunction", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./lambda_folder") # Replace with actual path to your Lambda code }), role=lambda_role.arn, handler="index.handler", # Replace with appropriate handler value for your Python function runtime="python3.8", # Replace with appropriate Python runtime for your Lambda )

    For the Lambda function code (which should be stored locally in ./lambda_folder and called index.py), you would have something like this:

    import boto3 import os def handler(event, context): # Your logic to check if the event is the completion of AI model training # This is where you check event details and determine if it's time to send an email # Create an SES client ses = boto3.client('ses', region_name=os.environ['AWS_REGION']) # Send email response = ses.send_email( Source='your-verified-email@example.com', Destination={ 'ToAddresses': ['recipient@example.com'], # Replace with your recipient addresses }, Message={ 'Subject': { 'Data': 'AI Model Training Completed', }, 'Body': { 'Text': { 'Data': 'Your AI model has finished training.', }, }, } ) return response

    The above is a high-level overview and some code examples. However, implementing a complete solution would involve additional steps, such as setting up the event trigger and handling the permissions and security aspects thoroughly.

    Remember to replace placeholder values with the actual information for your specific scenario. The above program will help you set up SES email notifications using Pulumi, but for a completely automated solution, more detail and integration with your AI training system would be required.