How can AWS SageMaker be used for conducting machine learning predictions with streaming data via Kinesis Data Streams in TypeScript

Question

Pulumi · Accepted Answer

To set up an AWS infrastructure that leverages SageMaker for conducting machine learning predictions with streaming data from Kinesis Data Streams using Pulumi in TypeScript, you'd typically follow these steps:

1. Create a Kinesis Data Stream that will capture streaming data in real-time.
2. Set up a SageMaker Model by creating an endpoint that will receive data for predictions.
3. Integrate the Kinesis Stream with the SageMaker Model, possibly using AWS Lambda to process the streamed data, invoke the SageMaker endpoint, and handle the predictions.

Below is a TypeScript program using Pulumi that sets up such an infrastructure. This example assumes you already have a trained machine learning model in SageMaker and will focus on creating the infrastructure to make real-time predictions with streaming data.

```typescript
import * as pulumi from '@pulumi/pulumi';
import * as aws from '@pulumi/aws';
import * as awsx from '@pulumi/awsx';

// Create a Kinesis Data Stream
const stream = new aws.kinesis.Stream("myDataStream", {
    shardCount: 1,
});

// Assuming a SageMaker model and endpoint configuration are already set up
// Replace `sagemakerModelName` and `sagemakerEndpointConfigName` with your resource names
const sagemakerModel = new aws.sagemaker.Model("myModel", {
    executionRoleArn: pulumi.interpolate`arn:aws:iam::${aws.getAccountId()}:role/SageMakerExecutionRole`,
    primaryContainer: {
        image: "174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1", // Example image URI
        modelDataUrl: "s3://my-bucket/model.tar.gz", // Replace with the S3 URL of your model
    },
});

const sagemakerEndpointConfig = new aws.sagemaker.EndpointConfiguration("myEndpointConfig", {
    productionVariants: [{
        modelName: sagemakerModel.name,
        variantName: "AllTraffic",
        initialInstanceCount: 1,
        instanceType: "ml.m4.xlarge",
    }],
});

const sagemakerEndpoint = new aws.sagemaker.Endpoint("myEndpoint", {
    endpointConfigName: sagemakerEndpointConfig.name,
});

// Create a Lambda function that will be triggered by the Kinesis Data Stream
const lambdaRole = new aws.iam.Role("lambdaRole", {
    assumeRolePolicy: aws.iam.assumeRolePolicyForPrincipal({ Service: "lambda.amazonaws.com" }),
});

const lambdaPolicyAttachment = new aws.iam.PolicyAttachment("lambdaPolicyAttachment", {
    roles: [lambdaRole],
    policyArn: aws.iam.ManagedPolicy.AWSLambdaKinesisExecutionRole,
});

const dataProcessingLambda = new aws.lambda.Function("dataProcessingLambda", {
    code: new pulumi.asset.AssetArchive({
        // Simple example: assume `index.js` exists and exports a handler function
        // Replace the content as necessary to process Kinesis data and call SageMaker
        "index.js": new pulumi.asset.FileAsset("index.js"),
    }),
    role: lambdaRole.arn,
    handler: "index.handler",
    runtime: aws.lambda.NodeJS12dXRuntime,
    environment: {
        variables: {
            SAGEMAKER_ENDPOINT_NAME: sagemakerEndpoint.endpointName,
        },
    },
    eventSourceMappings: [{
        eventSourceArn: stream.arn,
        startingPosition: "LATEST",
    }],
});

// Establish the necessary permissions for Lambda to invoke SageMaker
const lambdaInvokeSagemakerPolicy = new aws.iam.Policy("invokeSagemakerPolicy", {
    policy: pulumi.interpolate`{
        "Version": "2012-10-17",
        "Statement": [{
            "Effect": "Allow",
            "Action": "sagemaker:InvokeEndpoint",
            "Resource": "${sagemakerEndpoint.arn}"
        }]
    }`,
});

const lambdaInvokeSagemakerPolicyAttachment = new aws.iam.RolePolicyAttachment("lambdaInvokeSagemakerPolicyAttachment", {
    role: lambdaRole,
    policyArn: lambdaInvokeSagemakerPolicy.arn,
});

export const dataStreamName = stream.name;
export const sagemakerEndpointName = sagemakerEndpoint.endpointName;
export const lambdaFunctionName = dataProcessingLambda.name;
```

In this program:

- A Kinesis Data Stream is created, which can ingest streaming data such as clickstreams, application logs, and IoT telemetry data. ([AWS Kinesis Stream](https://www.pulumi.com/registry/packages/aws/api-docs/kinesis/stream/))
- A SageMaker Model, Endpoint Configuration, and Endpoint are assumed to be pre-configured and linked to your trained ML model. Replace the resource names and model data URLs with your actual resource details. ([AWS SageMaker Model](https://www.pulumi.com/registry/packages/aws/api-docs/sagemaker/model/))
- An AWS Lambda function is set up to be triggered by the Kinesis Data Stream events. It processes data and sends it to the SageMaker Endpoint for predictions in real-time. ([AWS Lambda](https://www.pulumi.com/registry/packages/aws/api-docs/lambda/function/))
- IAM Roles and Policies are configured to provide the necessary permissions to the Lambda function, allowing it to read from the Kinesis Stream and invoke the SageMaker Endpoint.

Please note that in a production environment, more considerations around security, error handling, and scaling should be taken into account. The example provided is for educational purposes and illustrates the basic setup.