S u c c e s s S t o r y
Deep Learning-based Sales Forecasting on AWS
Deep Learning-based Sales Forecasting on AWS
Build an End-to-End Automated ML Pipeline for Sales forecasting of more than two thousands Physical Stores
Retail
Sales forecasting is a crucial aspect of any business, and accurately predicting future sales
can help businesses make informed decisions and plan accordingly. However, forecasting sales
for over two thousand physical stores can be a challenging task. In this project, we aim to
tackle challenges by developing an end-to-end automated machine-learning pipeline that generates
an annual forecast every week.
To ensure accurate forecasting, our team will face several challenges. Firstly, with more than two
thousand physical stores, collecting and processing data can be a daunting task. To address this
challenge, we will leverage data augmentation techniques to generate more data and improve the
performance of our deep learning model. Secondly, engineering efforts will be required to ensure
the efficient processing of large amounts of data. We will also need to manage maintenance costs
to ensure the longevity of the system. Our deep learning model will need to achieve at least 80%
WMAPE to provide reliable and accurate forecasts. Finally, to maintain a steady stream of predictions,
we will need to build an automated end-to-end ML pipeline that can continuously generate forecasts every week.
At "Wolf of Data," we pride ourselves on providing innovative and reliable solutions to complex problems.
In this project, we tackled the challenge of sales forecasting for over two thousand physical stores
using deep learning. To achieve this goal, we leveraged the power of AWS-managed services for the
development and deployment of the model.
To ensure efficient and reliable forecasting, we developed an end-to-end ML pipeline using Amazon
Sagemaker pipelines. We implemented a single deep learning model to reduce engineering and maintenance
costs while still achieving an accuracy of more than 80% WMAPE for a significant number of stores.
To ensure a steady stream of predictions, we scheduled our sales forecasting engine to generate sales
forecasts of 52 weeks on a weekly basis. Additionally, to ensure the longevity of the system, our sales
forecasting model retrained itself on detecting any data or model drift.
In conclusion, by leveraging AWS-managed services and implementing a single deep learning model,
we were able to provide a cost-effective, reliable, and efficient sales forecasting solution. Our
end-to-end ML pipeline and automated scheduling ensured that the system was always up-to-date with
the latest data, providing accurate and reliable forecasts for over two thousand physical stores. At
"Wolf of Data," we are proud to have built a solution that has the potential to revolutionize the way
businesses forecast sales for physical stores.
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