What does this AWS Solutions Implementation do?
The Improving Forecast Accuracy with Machine Learning solution generates, tests, compares, and iterates on Amazon Forecast forecasts. The solution automatically produces forecasts and generates visualization dashboards for Amazon QuickSight or Amazon SageMaker Jupyter Notebooks—providing a quick, easy, drag-and-drop interface that displays time series input and forecasted output.
Forecasting can be applied to predict retail inventory demand, supply-chain planning, workforce status, web traffic forecasting, and more. The solution outputs probabilistic predictions at three default quantiles to address sensitivity to over and under-provisioning (shown in the sample forecasting graph to the right). You can also customize the forecasting predictions to meet your needs. Forecasts can be compared across dimensions (for example, retail store location) or item-level metadata (for example, product brand, size, and color). You can use this data for the following:
- Optimize existing forecasts: Save time and retain compatibility with your legacy tools, or gain insight into to over and under-provisioning, with the p50 forecast.
- Meet variable customer demand: Provide high levels of customer satisfaction with the p90 forecast, where the true value is expected to be lower than the predicted value 90% of the time.
- Avoid over-provisioning: Save on costs and avoid over-provisioning with the p10 forecast, where the true future demand value is expected to be lower than the predicted value only 10% of the time.
AWS Solutions Implementation overview
The diagram below presents the serverless architecture you can automatically deploy using the solution's implementation guide and accompanying AWS CloudFormation template.
Improving Forecast Accuracy with Machine Learning solution architecture
The AWS CloudFormation template deploys the resources required to automate your Amazon Forecast usage and deployments. Based on the capabilities of the solution, the architecture is divided into three parts: data preparation, forecasting, and data visualization.
The template includes an Amazon Simple Storage Service (Amazon S3) bucket for Amazon Forecast configuration; an Amazon S3 event notification that triggers when new datasets are uploaded to the related Amazon S3 bucket; an Improving Forecast Accuracy with Machine Learning AWS Step Functions state machine, with a series of AWS Lambda functions that build, train, and deploy your Machine Learning (ML) models in Amazon Forecast; and an Amazon Simple Notification Service (Amazon SNS) email subscription that notifies administrative users with the results of the AWS Step Functions.
The solution also includes Amazon CloudWatch metrics that track Amazon Forecast predictor accuracy metrics, and an Amazon SageMaker Notebook Instance that data scientists and developers can use to prepare and process data, and evaluate Forecast output.
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Automate manual processes
Jupyter Notebook integration
Amazon SNS notifications
Secure one-click deployment
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