1. Kinesis for ML Model Real-Time Inference Data Input


    Amazon Kinesis is a platform on AWS to process and analyze real-time streaming data. When integrating with machine learning (ML) models, Kinesis can be used to ingest and deliver the data for real-time inference. This is particularly useful for applications that need to make on-the-spot decisions based on live data streams, such as fraud detection, live recommendations, and more.

    We can use Pulumi to define and provision an AWS Kinesis Data Stream that can be connected to an ML model for real-time inference. The Kinesis Data Stream will accept data records that get produced continuously by data producers. These records can then be consumed by an AWS Lambda function that invokes an ML model for inference.

    Below is a Python program using Pulumi's AWS SDK. The program will set up a Kinesis Data Stream and an AWS Lambda function that can be used to process the stream's data in real-time. Note that to fully integrate with an ML model, you'd need to deploy the ML model as an AWS Lambda function and write the appropriate code within the Lambda to process the Kinesis records and invoke the ML model.

    First, let's install the necessary Pulumi AWS package by running the following command:

    pip install pulumi_aws

    Now, we will proceed with writing the Pulumi program:

    import pulumi import pulumi_aws as aws # Create a Kinesis Stream for data input kinesis_stream = aws.kinesis.Stream("ml_model_input_stream", shard_count=1, # Shard count determines the throughput of the stream retention_period=24, # How many hours the data records are accessible ) # Define an AWS Lambda function that will process the Kinesis stream's data # Note: Replace the `runtime`, `handler`, and `code` parameters with the actual # runtime and handler of your ML model, and provide the path to your deployment package. ml_model_processor = aws.lambda_.Function("ml_model_processor", runtime=aws.lambda_.Runtime.PYTHON3_8, # Assuming your ML model uses Python 3.8 handler="handler.entry_point", # The function within your code that receives the data code=pulumi.FileArchive("path_to_your_deployment_package.zip"), environment=aws.lambda_.FunctionEnvironmentArgs( variables={ "KINESIS_STREAM": kinesis_stream.name, # Pass the Kinesis stream name as an environment variable } ), timeout=120, # Maximum time the function has to process the data (in seconds) ) # Grant the Lambda function permissions to read from the Kinesis stream aws.lambda_.Permission("lambda_kinesis_permission", action="lambda:InvokeFunction", function=ml_model_processor.name, principal="kinesis.amazonaws.com", source_arn=kinesis_stream.arn, # The ARN (Amazon Resource Name) of the Kinesis stream ) # Create an Event Source Mapping that triggers the Lambda function for every record on the stream event_source_mapping = aws.lambda_.EventSourceMapping("ml_model_kinesis_event_mapping", batch_size=100, # Number of records to process in each batch enabled=True, # Start processing immediately event_source_arn=kinesis_stream.arn, starting_position="LATEST", # Start processing new records function_name=ml_model_processor.name, ) # Export the name of the Kinesis stream and the Lambda function ARN pulumi.export("kinesis_stream_name", kinesis_stream.name) pulumi.export("ml_model_processor_arn", ml_model_processor.arn)

    This program creates:

    1. Kinesis Data Stream: The data stream for real-time input that you will send data to, sharded for scalability. Each shard is a sequence of data records in a stream.

    2. AWS Lambda Function: This function represents your ML model. When creating the actual Lambda function, your ML model code should be able to process records from Kinesis Data Streams. In this example, the Lambda properties are placeholders; you'll need to specify your runtime (Python, Node.js, etc.), handler (the entry point for Lambda execution), and code (the location of your Lambda deployment package).

    3. Lambda Permission and Event Source Mapping: Permissions are established for the Lambda function to be invoked by the Kinesis stream, and an event source mapping is created to trigger the function with each incoming Kinesis record.

    4. Exports: The program exports the Kinesis stream name and the Lambda function ARN so they can be easily retrieved and used, such as setting up monitoring, more complex event processing, or integration with other services.

    After setting up the program, you would run pulumi up to create the infrastructure specified in your code. Ensure you have appropriate AWS credentials configured where Pulumi can access them.

    Remember that the given function handler and code are placeholder values. You should replace these with your real handler and deployment package, which include your ML model and code logic for processing Kinesis data records.