Use case in production
Using Transformer Models to Predict Demand at Scale in Production
AI Engineer, DataDrivers
Kai Martinen currently works as an AI Engineer at Datadrivers, focusing on natural language processing and time series analysis. In the last 3 years, he successfully managed and implemented several machine learning systems for customers in the logistics and e-commerce domain. He holds an M.S. and B.S. in Computer Science.
We implemented a machine learning system using Google’s Temporal Fusion Transformer to forecast customer demand in the online shop of a large omnichannel retailer. The ML system ensures that customer demand can always be served from the primary warehouse, even if articles may be in high demand. The training data combines categorical and real-valued time series data from multiple internal sources. The ML system is built on the Google Cloud Platform, using various cloud-native services.