Deep learning Sales forecasting on AWS by Wolf of Data

S u c c e s s   S t o r y

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



Technology Used

  • Programming Languages: Python
  • Machine Learning: Scikit, Pandas, NumPy
  • Deep Learning: Tensorflow
  • Cloud: AWS
  • Cloud Storage: AWS S3
  • CI/CD: Amazon Sagemaker Pipeline


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.

Impact we created

  • - Built Scaled Sales Forecasting Engine
  • - Save Client's annual loss of more than 200K USD
  • - A single Deep learning model for >2K stores
  • - Client's technical team productivity increases by 30%
  • - Automated mechanism for detecting data/model drift
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