1. Real-time User Feedback Analysis with AWS Lambda and ML


    To set up a real-time user feedback analysis system with AWS Lambda and machine learning (ML), we will use several AWS services together with Pulumi to create an infrastructure that can:

    1. Collect user feedback in real-time.
    2. Process and analyze the feedback data using AWS Lambda.
    3. Employ a machine learning model to gain insights from the data.

    For this, we'll use the following AWS services, orchestrated by Pulumi:

    • Amazon Kinesis: To ingest and temporarily store the real-time streaming data (user feedback).
    • AWS Lambda: To process the data from the Kinesis stream and to run the machine learning inference.
    • AWS IAM: To assign the necessary permissions for Lambda to access Kinesis streams and other required services.
    • Amazon S3: Optionally, to store the processed results or any additional data.
    • Amazon SageMaker: If a custom ML model is required, trained ML models can be deployed using SageMaker.

    Here is how the setup will work:

    1. User feedback will be sent to an AWS Kinesis Stream. This is a scalable and durable real-time data streaming service.
    2. A Lambda Function will be triggered in response to data added to the Kinesis Stream.
    3. The Lambda function will process the data, possibly using a trained machine learning model deployed through Amazon SageMaker or another ML service.
    4. After analysis, the Lambda function can store results in Amazon S3 for long-term storage or further analysis.

    Let's start with a Pulumi program that sets up these components in AWS using the Python language:

    import pulumi import pulumi_aws as aws # Creates an AWS Kinesis Stream for ingesting user feedback in real-time. feedback_stream = aws.kinesis.Stream("feedbackStream", shard_count=1, # The optimal shard count varies based on the expected data volume ) # IAM role that allows Lambda to access the Kinesis Stream and CloudWatch Logs lambda_role = aws.iam.Role("lambdaRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] }""" ) # Policy attachment that grants the Lambda function access to Kinesis and CloudWatch # Adjust the permissions as necessary based on the specific needs of your application policy_attachment = aws.iam.PolicyAttachment("lambdaPolicyAttachment", roles=[lambda_role], policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaKinesisExecutionRole" ) # AWS Lambda Function that will process the user feedback feedback_processor = aws.lambda_.Function("feedbackProcessor", runtime="python3.8", # Ensure that the runtime matches the one required for your ML application role=lambda_role.arn, handler="handler.process_feedback", # The file and method that will process feedback code=pulumi.AssetArchive({ # Replace with the path to the code for your Lambda function ".": pulumi.FileArchive("./path_to_your_lambda_function_code") }), # Trigger the Lambda function when new records are available in the stream event_sources=[aws.lambda_.EventSourceMappingArgs( event_source_arn=feedback_stream.arn, starting_position="LATEST" )] ) # Optionally, if you want to have permanent storage for the processed data, set up an S3 bucket results_bucket = aws.s3.Bucket("resultsBucket", acl="private" # Adjust the ACL based on your needs ) # Output the important URNs and ARNs that can be used to further interact with the resources pulumi.export("feedback_stream_arn", feedback_stream.arn) pulumi.export("feedback_processor_arn", feedback_processor.arn) pulumi.export("results_bucket_name", results_bucket.bucket)

    In this program:

    • We create a Kinesis Stream for the user to send feedback to.
    • An IAM role is created to enable Lambda to read from Kinesis and write logs to CloudWatch.
    • The Lambda function is deployed to process the feedback data.
      • You need to have the Lambda function code ready, which should include loading the ML model and processing the data. Replace ./path_to_your_lambda_function_code with the actual path to your Lambda function's code.
      • handler.process_feedback is a placeholder for your Lambda function handler and method. You should replace it with your actual handler name and method.
    • We also create an S3 bucket in case the results need to be stored; you can omit this if not required.

    With this setup, any user feedback sent to the Kinesis Stream will be automatically processed by the Lambda function in real-time, allowing for immediate analysis and reaction to user feedback.