Personalized recommendations using AWS FinSpace

The goal of providing personalized recommendations using AWS FinSpace could be accomplished using several resources. From the query to the Pulumi Registry, the central component seems to be the AWS FinSpace environment, created using the aws-native.finspace.Environment resource. We'd also probably manage data with FinSpace data bundles and ensure security with AWS KMS.

Given this, here's a very simplified example of how you might create a FinSpace environment. Please note that due to the high variability and complexity of financial data scenarios, this script merely scratches the surface, and you'd have to dive deeper to fully develop a personalized recommendations system.

import * as pulumi from "@pulumi/pulumi";
import * as finspace from "@pulumi/aws-native/finspace";

// Create a Finspace environment. 
let environment = new finspace.Environment("my-environment", {
    kmsKeyId: 'your-kms-key-id', // Insert your KMS KeyId here
    name: "my-environment",
    description: "A FinSpace environment",
    federationMode: "AWS_SSO", // For AWS SSO authentication
    superuserParameters: {
        emailAddress: "admin@domain.com",
        firstName: "Super",
        lastName: "User",
    }
});

// Export the environment's properties
export const environmentId = environment.id;

Please refer to the aws-native.finspace.Environment documentation for more detail about the fields used above.

A full-fledged personalization system would require more setup, including configuring your data sources and bundles, setting up machine learning or analytics services to provide recommendations, such as AWS SageMaker or EMR (Elastic MapReduce), and possibly investing in additional AWS-native packages for data management and processing. This could involve additional Pulumi resources beyond the aws-native.finspace.Environment shown above.

Based on your context and use case, would you like to incrementally include and discuss these services?