Implementing an End-to-End Demand Forecasting Solution Through Databricks and MLflow

In retail, the right quantity at the right time is crucial for success. In this session we share how a demand forecasting solution helped some of our retailers to improve efficiencies and sharpen fresh product production and delivery planning.

With the setup in place we train hundreds of models in parallel, training on various levels including store level, product level and the combination of the two. By leveraging the distributed computation of Spark, we can do all of this in a scalable and fast way. Powered by Delta Lake, feature store and MLFlow this session clarifies how we built a highly reliable ML factory.

We show how this setup runs at various retailers and feeds accurate demand forecasts back to the ERP system, supporting the clients in their production planning and delivery. Through this session we want to inspire retailers & conference attendants to use data & AI to not only gain efficiency but also decrease food waste.

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