In this talk, we will talk through the journey of building an end-to-end forecasting platform with a focus on feature engineering, consolidation of features in a single place (feature store), and leveraging DNN techniques to solve different forecasting problems like Demand forecasting, Replenishment, Inventory optimization, etc. powered through the same platform

As part of the session, we will review the journey with the following stages :

• Feature Engineering: Experimentation to add critical features like Product Hierarchies, Price Rank Ratios, historical aggregated features, etc. along with their impact on the model accuracy
• MLOps : Use open-source Kubeflow to orchestrate and automate the data load, feature engineering, and Model training along with Model Deployment
• Decomposition of Forecast: Decompose forecast into Base & Promo forecast for What-if Simulation to identify the impact of Promos on Forecasts. Also, expanding the approach to inventory optimization.