Real-time User Feedback Analysis with AWS Lambda and ML

Question

Pulumi · Accepted Answer

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:

```python
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.