Amazon Forecast just launched the ability to hierarchically delete resources at a parent level without having to locate the child resources. You can stay focused on building value-adding forecasting systems and not worry about trying to manage individual resources that are created in your workflow. Forecast uses machine learning (ML) to generate more accurate demand forecasts, without requiring any prior ML experience. Forecast brings the same technology used at Amazon.com to developers as a fully managed service, removing the need to manage resources or rebuild your systems.
When importing data, training a predictor, and creating forecasts, Forecast generates resources related to the dataset group. For example, when a predictor is generated using a dataset group, the predictor is the child resource and the dataset group is the parent resource. Previously, it was difficult to delete resources while building your forecasting system because you had to delete the child resources first, and then delete the parent resources. This was especially difficult and time-consuming because deleting resources required you to understand the various resource hierarchies, which weren’t immediately visible.
As you experiment and create multiple dataset groups, predictors, and forecasts, the resource hierarchy can become complicated. However, this streamlined hierarchical deletion method allows you to quickly clean up resources without having to worry about understanding the resource hierarchy.
In this post, we walk through the Forecast console experience of deleting all the resource types that are supported by Forecast. You can also perform hierarchical deletion by referencing the Deleting Resources page. To delete individual or child resources one at a time, you can continue to use the existing APIs such as DeleteDataset, DeleteDatasetGroup, DeleteDatasetImportJob, DeleteForecast, DeleteForecastExportJob, DeletePredictor and DeletePredictorBacktestExportJob.
Delete dataset group resources
To delete a dataset group when it doesn’t have any child resources, a simple dialog is displayed. You can delete the chosen resource by entering delete and choosing Delete.
When a dataset group has underlying child resources such as predictors, predictor backtest export jobs, forecasts, and forecast export jobs, a different dialog is displayed. After you enter delete and choose Delete, all these child resources are deleted, including the selected dataset group resource.
Delete dataset resources
For a dataset resource without child resources, you see a simple dialog is during the delete operation.
When a dataset has child dataset import jobs, the following dialog is displayed.
Delete predictor resources
For a predictor resource without child resources, the following simple dialog is displayed.
When the predictor resource has underlying child resources such as predictor backtest export jobs, forecasts, or forecast export jobs, the following dialog is displayed. If you proceed with the delete action, all these child resources are deleted, including the selected predictor resource.
Delete a forecast resource
For a forecast resource without child resources, the following dialog is displayed.
When a forecast resource has underlying child resources such as forecast export jobs, the following dialog is displayed.
Delete dataset import job, predictor backtest export job, or forecast export job resources
The dataset import job, predictor backtest export job, and forecast export job resources don’t have any child resources. Therefore, when you choose to delete any of these resources via the Forecast console, a simple delete dialog is displayed. When you proceed with the delete, only the selected resources are deleted.
For example, when deleting a dataset import job resource, the following dialog is displayed.
You now have more flexibility when deleting a resource or an entire hierarchy of resources. To get started with this capability, see the Deleting Resources page and go through the notebook in our GitHub repo that walks you through how to perform hierarchical deletion. You can use this capability in all Regions where Forecast is publicly available. For more information about Region availability, see AWS Regional Services.
About the Authors
Alex Kim is a Sr. Product Manager for Amazon Forecast. His mission is to deliver AI/ML solutions to all customers who can benefit from it. In his free time, he enjoys all types of sports and discovering new places to eat.
Ranga Reddy Pallelra works as an SDE on the Amazon Forecast team. In his current role, he works on large-scale distributed systems with a focus on AI/ML. In his free time, he enjoys listening to music, watching movies, and playing racquetball.
Shannon Killingsworth is a UX Designer for Amazon Forecast and Amazon Personalize. His current work is creating console experiences that are usable by anyone, and integrating new features into the console experience. In his spare time, he is a fitness and automobile enthusiast.