1. How to use data from Amazon RDS for building a churn prediction model using AWS SageMaker and visualize it in Tableau in TypeScript


    To accomplish your goal of building a churn prediction model using Amazon RDS data with AWS SageMaker and then visualizing it in Tableau, you need to stitch together several AWS services. In this guide, I'll walk you through setting up these services using Pulumi with TypeScript.

    Before you start, make sure you have Pulumi set up with the appropriate AWS credentials configured. Also, ensure you have AWS Command Line Interface (AWS CLI) installed and configured as Pulumi relies on the AWS CLI configuration for authentication.

    The process can be divided into the following steps:

    1. Setting up an Amazon RDS Instance: We'll provision an RDS instance that holds your data.
    2. Connecting SageMaker to RDS: We'll create a SageMaker instance and connect it to the RDS database.
    3. Building the churn prediction model: We'll define a SageMaker pipeline that processes the RDS data and builds the prediction model.
    4. Visualizing in Tableau: While Pulumi does not directly integrate with Tableau, I'll touch upon how to make the data available for visualization. Tableau can connect to RDS or use a processed dataset that lives in AWS S3 or another supported AWS data store.

    Step 1: Setting up an Amazon RDS Instance

    First, we'll set up an Amazon RDS instance. For simplicity, I will use a PostgreSQL engine, but you are free to choose the engine that best matches your use case.

    import * as pulumi from '@pulumi/pulumi'; import * as aws from '@pulumi/aws'; // Create a new RDS instance const dbInstance = new aws.rds.Instance('my-churn-db', { allocatedStorage: 20, engine: 'postgres', engineVersion: '12.4', instanceClass: 'db.t2.micro', name: 'churnDB', parameterGroupName: 'default.postgres12', password: 'mySuperSecretPassword', skipFinalSnapshot: true, username: 'postgres', }); // Export the RDS instance endpoint export const dbEndpoint = dbInstance.endpoint;

    Amazon RDS Instance documentation

    Step 2: Connecting SageMaker to RDS

    Next, let's set up an AWS SageMaker instance that can use SQL to query the data from RDS.

    import * as sagemaker from '@pulumi/aws/sagemaker'; // Create a SageMaker notebook instance const notebookInstance = new sagemaker.NotebookInstance('mySageMakerInstance', { instanceType: 'ml.t2.medium', roleArn: aws.iamRole.example.arn, // Replace with an appropriate IAM role ARN }); // Export the SageMaker notebook instance URL export const notebookInstanceUrl = notebookInstance.url;

    AWS SageMaker NotebookInstance documentation

    Step 3: Building the Churn Prediction Model

    Let's define a pipeline in SageMaker that will prepare the data from RDS, train a churn prediction model, and evaluate the performance.

    const churnModelPipeline = new aws.sagemaker.Pipeline('churnModelPipeline', { pipelineName: 'churn-prediction-pipeline', pipelineDefinition: { // Define your SageMaker pipeline steps here // Pipeline steps typically include data preparation, model training, and model evaluation } }); // Export the SageMaker pipeline ARN export const pipelineArn = churnModelPipeline.pipelineArn;

    AWS SageMaker Pipeline documentation

    Step 4: Visualizing in Tableau

    Finally, Tableau setup is not possible with Pulumi as of the last knowledge cutoff in 2023. However, you can use Tableau by connecting directly to the RDS instance using its credentials or by reading from an S3 bucket if SageMaker outputs the results there.

    You would typically configure Tableau with the necessary connection details from the RDS or an AWS data source where your SageMaker outputs are saved.

    Remember, you'll need to take one additional manual step outside Pulumi: Configure your Tableau Desktop or Server to access RDS or use an S3 bucket (whichever you choose for outputting the SageMaker results) for visualization purposes. You can do this by creating a data source connection in Tableau to your RDS instance using the dbEndpoint output from the RDS setup or pointing to the S3 bucket location with your SageMaker results.

    This guide provides a starting point for automation. Remember to replace placeholders like IAM role ARNs, specify your SageMaker pipeline definition details, and fill out any other necessary configuration details for the AWS resources.

    Building, training, and deploying machine learning models might involve more intricate configurations, and as you get comfortable with Pulumi and AWS, you can explore more features that can help fine-tune and control your ML workflows and cloud resources.