Amazon Forecast now provides estimated run time for forecast creation jobs, enabling you to manage your time efficiently

Amazon Forecast now displays the estimated time it takes to complete an in-progress workflow for importing your data, training the predictor, and generating the forecast. You can now manage your time more efficiently and better plan for your next workflow around the estimated time remaining for your in-progress 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.

Previously, you had no clear insights as to how long a workflow would take to complete, which forced you to proactively monitor each stage, whether it was importing your data, training the predictor, or generating the forecast. This made it difficult for you to plan for subsequent steps, causing frustration and anxiety. This can be especially frustrating when the time required to import data, train a predictor, and creating forecasts can vary widely depending on the size and characteristics of your data.

Now, you have visibility into the time that a workflow may take, which can be especially useful for manually running your forecast workloads and during the process of experimentation. Knowing how long each workflow will take allows you to focus on other tasks and come back to the forecast journey later. Additionally, the displayed estimated time to complete a workflow refreshes automatically, which provides better expectations and removes further frustration.

In this post, we walk through the Forecast console experience of reading the estimated time to workflow completion. To check the estimated time through the APIs, refer to DescribeDatasetImportJob, DescribePredictor, DescribeForecast.

If you want to build automated workflows for Forecast, we recommend following the steps outlined in Create forecasting systems faster with automated workflows and notifications in Amazon Forecast, which walks through integrating Forecast with Amazon EventBridge to build event-driven Forecast workflows. EventBridge removes the need to manually check the estimated time for a workflow to complete, because it starts your desired next workflow automatically.

Check the estimated time to completion of your dataset import workflow

After you create a new dataset import job, you can see the Create pending status for the newly created job. When the status changes to Create in progress, you can see the estimated time remaining in the Status column of the Datasets imports section. This estimated time refreshes automatically until the status changes to Active.

On the details page of the newly created dataset import job, when the status is Create in progress, the Estimated time remaining field shows the remaining time for the import job to complete and Actual import time shows -. This section refreshes automatically with the estimated time to completion. After the import job is complete and the status becomes Active, the Actual import time shows the total time of the import.

Check the estimated time to completion of your predictor training workflow

After you create a new predictor, you first see the Create pending status for the newly created job. When the status changes to Create in progress, you see the estimated time remaining in the Status column in the Predictors section. This estimated time refreshes automatically until the status changes to Active.

On the details page of the newly created predictor job, when the status is Create in progress, the Estimated time remaining field shows the remaining time for the predictor job to complete and Actual import time shows -. This section refreshes automatically with the estimated time to completion. After the import job is complete and the status becomes Active, the Actual import time shows the total time for the predictor creation.

Check the estimated time to completion of your forecast creation workflow

After you create a new forecast, you first see the Create pending status for the newly created job. When the status changes to Create in progress, you see the estimated time remaining in the Status column. This estimated time refreshes automatically until it changes to Active.

On the details page of the newly created forecast job, when the status is Create in progress, the Estimated time remaining field shows the remaining time for the forecast job to complete and Actual import time shows -. This section refreshes automatically with the estimated time to completion. After the import job is complete and the status changes to Active, the Actual import time shows the total time for the forecast creation to complete.

Conclusion

You can now find out how long it takes when you initiate a workload using Forecast, which can help you manage your time more efficiently. The new field is part of the response to Describe* calls that will show up automatically, without requiring any setup.

To learn more about this capability, see DescribeDatasetImportJob, DescribePredictor, and DescribeForecast. 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.

Ranjith Kumar Bodla is an SDE in the Amazon Forecast team. He works as a backend developer within a distributed environment with a focus on AI/ML and leadership. During his spare time, he enjoys playing table tennis, traveling, and reading.

Gautam Puri is a Software Development Engineer on the Amazon Forecast team. His focus area is on building distributed systems that solve machine learning problems. In his free time, he enjoys hiking and basketball.

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.

Anurag Pant is a Software Development Engineer in the Amazon Forecast team. He leverages his interest in machine learning and data science to work on large-scale distributed systems in the forecasting space. In his free time, he enjoys travelling, hiking and playing video games.