Real-Time Machine Learning Inference with AWS Kinesis Analytics

Question

Pulumi · Accepted Answer

To create a real-time machine learning inference system using AWS, you can leverage various AWS services and features. At the core of real-time data processing is AWS Kinesis, which can collect, process, and analyze real-time data streams. Kinesis Analytics is specifically designed to process and analyze stream data using standard SQL queries, which is particularly useful when building machine learning applications that need to respond to incoming data in real-time.

The following Pulumi program sets up a Kinesis Analytics Application, which continuously reads and processes data from a Kinesis data stream. While the specifics of the machine learning inference logic will depend on your use case (and typically involve integrating with AWS SageMaker or similar services), this program gives you the basic infrastructure to start ingesting and processing data in real-time with Pulumi and AWS.

Let's break it down step by step:

1. **Kinesis Data Stream**: We'll begin by setting up a Kinesis stream that will serve as the source of the real-time data. Data producers will push records to this stream.

2. **IAM Role**: A necessary component is an IAM role that Kinesis Analytics can assume to access other AWS resources. In this example, this role allows Kinesis Analytics to read from the Kinesis stream.

3. **Kinesis Analytics Application**: This is the central piece that consumes the stream and performs real-time analytics. You can input the SQL code directly or use a Lambda function to process the data.

Now, let's look at the program that sets up this infrastructure:

```python
import pulumi
import pulumi_aws as aws

# Create an Amazon Kinesis Stream that we'll process in real-time
kinesis_stream = aws.kinesis.Stream("exampleStream",
    shard_count=1)

# IAM Role that allows Kinesis Analytics to access the stream
analytics_role = aws.iam.Role("analyticsRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {"Service": "kinesisanalytics.amazonaws.com"}
        }]
    }""")

# Attach a policy to the IAM role
stream_read_policy = aws.iam.RolePolicy("streamReadPolicy",
    role=analytics_role.name,
    policy=kinesis_stream.arn.apply(lambda arn: f"""{{
        "Version": "2012-10-17",
        "Statement": [{{
            "Effect": "Allow",
            "Action": ["kinesis:DescribeStream", "kinesis:GetShardIterator", "kinesis:GetRecords"],
            "Resource": "{arn}"
        }}]
    }}"""))

# Set up the Kinesis Analytics Application.
# This resource lets you process the stream using standard SQL, which is ideal for real-time analytics.
analytics_app = aws.kinesis.AnalyticsApplication("exampleAnalyticsApp",
    inputs={
        "namePrefix": "example_prefix",
        "kinesisStream": {
            "resourceArn": kinesis_stream.arn,
            "roleArn": analytics_role.arn
        },
        "schema": {
            "recordColumns": [
                {
                    "name": "example_col",
                    "sqlType": "VARCHAR(64)",
                    "mapping": "$.example"  # Mapping to the column in the incoming JSON from the stream
                }
            ],
            "recordFormat": {
                "recordFormatType": "JSON",
                "mappingParameters": {
                    "json": {
                        "recordRowPath": "$"  # The path to the element in the JSON that contains record fields
                    }
                }
            }
        }
    },
    # The SQL code to process the stream will be specified here.
    # This is just an example stub to show where you would put your SQL query or model call.
    # Replace it with actual SQL to process the stream according to your needs.
    code="""
    CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
        "example_col" VARCHAR(64)
    );
    CREATE OR REPLACE PUMP "STREAM_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM"
    SELECT STREAM "example_col"
    FROM "SOURCE_SQL_STREAM_001";
    """
)

# Export the Kinesis Stream name and the Analytics Application name
pulumi.export('kinesisStreamName', kinesis_stream.name)
pulumi.export('analyticsAppName', analytics_app.name)
```

In the above program:

- We defined a Kinesis stream (`exampleStream`) that acts as the source of the real-time data.
- We then created an IAM role (`analyticsRole`) that Kinesis Analytics can assume to read from the Kinesis stream.
- We attached a policy (`streamReadPolicy`) to the IAM role, allowing access to the Kinesis stream.
- Finally, we created the Kinesis Analytics Application (`exampleAnalyticsApp`), defining inputs, the necessary schema mapping, and the location where the incoming data will be processed (in the SQL code block).

Please note that the real-time processing logic, such as the actual SQL code or machine learning model invocation, is not included in this program and would need to be implemented to suit your specific use case. The SQL code block in the `analytics_app` resource is a placeholder where the actual data processing or machine learning inference would occur.

Each resource in the program includes detailed information from the [Pulumi AWS documentation](https://www.pulumi.com/docs/), making it clear how to define the properties and use them correctly.

To get started with this setup, simply run `$ pulumi up` after writing this script to a file (e.g., `main.py`) and making sure you've set up your AWS credentials correctly in your Pulumi stack configuration.