We are no longer updating the Amazon Machine Learning service or accepting new users for it. This documentation is available for existing users, but we are no longer updating it. For more information, see What is Amazon Machine Learning.
Step 6: Clean Up
To avoid accruing additional Amazon Simple Storage Service (Amazon S3) charges, delete the data stored in Amazon S3. You are not charged for other unused Amazon ML resources, but we recommend that you delete them to keep your workspace clean.
To delete the input data stored in Amazon S3
Open the Amazon S3 console at https://console.aws.amazon.com/s3/
. -
Navigate to the Amazon S3 location where you stored the
banking.csvandbanking-batch.csvfiles. -
Select the
banking.csv,banking-batch.csv, and.writePermissionCheck.tmpfiles. -
Choose Actions, and then choose Delete.
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When prompted for confirmation, choose OK.
Although you aren't charged for keeping the record of the batch prediction that Amazon ML ran or the datasources, model, and evaluation that you created during the tutorial, we recommend that you delete them to prevent cluttering your workspace.
To delete the batch predictions
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Navigate to the Amazon S3 location where you stored the output of the batch prediction.
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Choose the
batch-predictionfolder. -
Choose Actions, and then choose Delete.
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When prompted for confirmation, choose OK.
To delete the Amazon ML resources
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On the Amazon ML dashboard, select the following resources.
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The
Banking Data 1datasource -
The
Banking Data 1_[percentBegin=0, percentEnd=70, strategy=sequential]datasource -
The
Banking Data 1_[percentBegin=70, percentEnd=100, strategy=sequential]datasource -
The
Banking Data 2datasource -
The
ML model: Banking Data 1ML model -
The
Evaluation: ML model: Banking Data 1evaluation
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Choose Actions, and then choose Delete.
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In the dialog box, choose Delete to delete all selected resources.
You have now successfully completed the tutorial. To continue using the console to create datasources, models, and predictions see the Amazon Machine Learning Developer Guide. To learn how to use the API, see the Amazon Machine Learning API Reference.
