Launching Data Wrangler from Amazon Personalize - Amazon Personalize

Launching Data Wrangler from Amazon Personalize

To launch Data Wrangler from Amazon Personalize, you use the Amazon Personalize console to configure a SageMaker domain and launch Data Wrangler.

To launch Data Wrangler from Amazon Personalize
  1. Open the Amazon Personalize console at https://console.aws.amazon.com/personalize/home and sign in to your account.

  2. On the Dataset groups page, choose your dataset group.

  3. In Set up datasets choose Create dataset and choose the type of dataset to create. You can't use Data Wrangler to prepare an Actions dataset or Action interactions dataset.

  4. Choose Import data using Data Wrangler and choose Next.

  5. For SageMaker domain, choose to use an existing domain or create a new one. You need a SageMaker Domain to access Data Wrangler in SageMaker Studio Classic. For information about domains and user profiles, see SageMaker Domain in the Amazon SageMaker Developer Guide.

  6. To use an existing domain, choose a SageMaker domain and User profile to configure the domain.

  7. To create a new domain:

    • Give the new domain a name.

    • Choose a User profile name.

    • For Execution role, choose the role you created in Setting up permissions. Or, if you have CreateRole permissions, create a new role using the role creation wizard. The role you use must have the AmazonSageMakerFullAccess policy attached.

  8. Choose Next. If you are creating a new domain, SageMaker starts creating your domain. This can take up to ten minutes.

  9. Review the details for your SageMaker domain.

  10. Choose Import data with Data Wrangler. SageMaker Studio Classic starts creating your environment, and when complete, the Data flow page of Data Wrangler in SageMaker Studio Classic opens in a new tab. It can take up to five minutes for SageMaker Studio Classic to finish creating your environment. When it finishes, you are ready to start importing data into Data Wrangler. For more information, see Importing data into Data Wrangler.