Bastian Wandt
Bastian is a Senior Machine Learning Research Engineer at idealo Internet GmbH, where he focuses on large-scale offer cataloging and high-throughput machine learning systems. Before joining idealo in 2025, he was an Assistant Professor at Linköping University in Sweden, leading a research group in 3D computer vision.
He completed his PhD in 2020 at Leibniz University Hannover with a thesis on 3D human pose estimation and subsequently spent two years at the University of British Columbia in Canada as a PostDoc, expanding his research into broader areas of 3D computer vision and teaching related courses.
Session
E-commerce cataloging at idealo operates at extreme scale: 4.5 billion offers from 50,000+ shops across six countries, with peak ingestion rates of 4.8 million offers per minute. While large language models (LLMs) provide strong classification accuracy, they are too slow and costly for billion-scale real-time processing. This talk shows how idealo builds a cost-efficient, high-throughput machine learning system that leverages LLM knowledge without deploying full models in production.
We present how knowledge distillation from a large e5 instruction model enables a compact multilingual MiniLM encoder to achieve high accuracy, and how optimized inference runtimes and specialized hardware such as AWS Neuron help meet strict latency and cost requirements. Beyond modeling, we highlight key operational challenges: constructing training datasets from massively imbalanced data, selecting the right encoder architecture from today’s model landscape, and designing a robust MLOps lifecycle with automated data sampling, training, deployment, and monitoring.
Attendees will learn practical techniques for scaling ML systems under real-world constraints, how to extract value from LLMs when they are too large to serve directly, and how to transition research prototypes into reliable, high-volume production pipelines.