Clean-up and next steps


You can also find the sample jupyter notebook you used to do the lab here.


  • Terminate the provisioned product to avoid getting charged ~121.00$ a month [~$0.0464/hr
    • for ml.t2.medium notebook instance +$0.115/hr for the endpoint apart from S3 charges]. You can always come back and deploy the App product how you did today. To terminate provisioned product:
    • Open AWS Service Catalog.
    • Choose Provisioned Products from the panel on the left.
    • Select Provisioned Product with name as App.
    • Choose Actions.
    • Choose Terminate.
    • Choose Terminate Provisioned Product:
  • You can optionally terminate the cloudformation stack you created to set up this workshop. The cloudformation stack primarily created AWS Service catalog portfolio and products and is inexpensive as it charges based on number of API calls (~14 calls for 1 cent).

Next steps

  • To come out of _service_catalog_enduser assumed role:
    • Choose Student in top-right corner of your AWS console.
    • Choose Back to admin.
  • Explore AWS Data Exchange and identify which product can you help you build differentiating features:
  • Explore AWSMarketplace and identify ML Models that can help you in your organization: