Introducing AWS Solution: Improving Forecast Accuracy with Machine Learning
The Improving Forecast Accuracy with Machine Learning is an AWS Solutions Implementation that provides an automated pipeline for generating, testing, and comparing Amazon Forecast models and forecasts allowing developers and data scientists to reduce either the overhead costs of developing new forecasts or the time required to optimize existing ones.
The Improving Forecast Accuracy with Machine Learning is an AWS Solutions Implementation that provides an automated pipeline for generating, testing, and comparing Amazon Forecast models and forecasts allowing developers and data scientists to reduce either the overhead costs of developing new forecasts or the time required to optimize existing ones.
Forecasting is an essential business function that allows organizations to develop informed strategies based on historical demand data. Predicting future demand can be critical for running an efficient business because accurate demand forecasting helps minimize over and under-provisioning thereby optimizing profitability and increasing customer satisfaction. Forecasting can be applied to multiple use cases including predicting retail product demand, supply-chain planning, and resource planning to name a few.
Businesses can configure this solution, then drag-and-drop historical demand data into Amazon Simple Storage Service (Amazon S3) to generate forecasts as well as evaluate the impact of external variables (e.g. price, promotion) and associated ‘what-if’ scenarios. Additionally, customers can then visualize results in the included Amazon SageMaker Jupyter Notebook. To learn more about the Improving Forecast Accuracy with Machine Learning solution, see the AWS Solutions Implementation webpage.
Additional AWS Solutions are available on the AWS Solutions Implementation webpage, where customers can browse solutions by product category or industry to find AWS-vetted, automated, turnkey reference implementations that address specific business needs.