What is the process for packaging and deploying models using new tools in Amazon SageMaker in TypeScript

Question

Pulumi · Accepted Answer

Packaging and deploying models with Amazon SageMaker involve several steps, which include creating a model package, setting up the model, and deploying it to an endpoint for real-time inference or creating a batch transform job for batch processing.

To implement this using Pulumi and AWS SDK, you will generally go through the following steps:

1. **Define the Model Package**: You'll leverage the `aws-native.sagemaker.ModelPackage` resource to define a model package that encapsulates the model and its associated metadata.

2. **Create the Model**: Using the `aws.sagemaker.Model` resource, you'll create a model in SageMaker based on the model package.

3. **Deploy the Model**: For real-time inference, you will create an endpoint configuration using the `aws.sagemaker.EndpointConfiguration` resource and then deploy the model to an endpoint using the `aws.sagemaker.Endpoint`. For batch inference, you'll set up a batch transform job using the `aws.sagemaker.TransformJob`.

Here is a TypeScript program that outlines these steps:

```typescript
import * as pulumi from "@pulumi/pulumi";
import * as aws from "@pulumi/aws";

// Define the necessary variables for resource creation, e.g., S3 bucket for model data
const s3BucketName = "my-model-data-bucket";
const modelName = "my-model";
const roleArn = "arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001";

// Typically, your model data would be in an S3 bucket after training
const modelDataUrl = `s3://${s3BucketName}/model.tar.gz`;

// Define the model package, assuming that you've already created a model package group
const modelPackage = new aws.sagemaker.ModelPackage("modelPackage", {
  // ... your specific parameters and configuration
});

// Create the SageMaker model
const sageMakerModel = new aws.sagemaker.Model("myModel", {
  executionRoleArn: roleArn,
  primaryContainer: {
    image: "763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-inference:2.3.0-cpu",
    modelDataUrl: modelDataUrl,
  },
  name: modelName,
});

// Deploy the model by creating an endpoint configuration and then deploying to an endpoint
const endpointConfig = new aws.sagemaker.EndpointConfiguration("myEndpointConfig", {
  productionVariants: [{
    instanceType: "ml.m4.xlarge",
    modelName: sageMakerModel.name,
    initialInstanceCount: 1,
    variantName: "AllTraffic",
  }],
  name: pulumi.interpolate(`${modelName}-endpoint-config`),
});

// Create the SageMaker endpoint
const endpoint = new aws.sagemaker.Endpoint("myEndpoint", {
  endpointConfigName: endpointConfig.name,
  name: pulumi.interpolate(`${modelName}-endpoint`),
});

// Exports the endpoint name and URL for easy access
export const endpointName = endpoint.name;
export const endpointUrl = pulumi.interpolate(`https://${endpoint.name}.sagemaker.{region}.amazonaws.com`);

```

This Pulumi program sets up a basic infrastructure for packaging and deploying a model in SageMaker. You would need to replace placeholders with actual values suitable for your specific use case. Also, remember that interactions with AWS services incur costs, make sure to understand the cost implications and have the necessary permissions set up in IAM.

Keep in mind that for a production-grade solution, you might need to include additional configurations such as VPC settings, security groups, and other security and network configurations.