Stefan Birr
Senior Applied Scientist at Zalando, working on developing large scale forecasting systems. Stefan holds a PhD in Mathematics from Ruhr University Bochum where his research focused on "Analyzing dynamic dependencies in time series. Prior to his 3 years at Zalando he worked for 5 years at E.ON as a Data Scientist creating algorithms for smart meter analytics and forecasting.
Session
How do you evaluate performance when you predict more than 10 million time series each day? While a good plot can be worth more than a thousand metrics for a single time series, with large-scale machine learning models implemented with LightGBM and PyTorch we have to resort to meaningful aggregations. We will share insights and learnings from the past 2 years of deploying and operating our article-level demand forecasting models at the pricing department of Zalando.
This talk moves beyond basic metrics to showcase the pitfalls of aggregated error measures and the best practices we’ve developed to keep our stakeholders informed and our models accurate.