Running machine learning models in a production environment brings its own challenges. In this talk we would like to present our solution of a machine learning lifecycle for the text-based cataloging classification system from idealo.de. We will share lessons learned and talk about our experiences during the lifecycle migration from a hosted cluster to a cloud solution within the last 3 years. In addition, we will outline how we embedded our ML components as part of the overall idealo.de processing architecture.

Gunar Maiwald

Affiliation: idealo internet GmbH

Gunar Maiwald has a background in Computer Science. For the last 3 years he worked as an ML engineer at idealo.de. His professional programming path led him from Perl via TypeScript to Python.

Tobias Senst

Affiliation: idealo internet GmbH

Tobias Senst is a Senior Machine Learning Engineer at idealo internet GmbH. Tobias Senst received his PhD in 2019 from the Technische Universität Berlin under the supervision of Prof. Thomas Sikora. He has more than 10 years of experience in Computer Vision and Video Analytics research.

At idealo, he switched from the world of images and videos to Natural Language Processing and is responsible for the operation and development of machine learning models in a productive environment.

• LinkedIn: https://linkedin.com/in/tobias-senst-08090b192
• Github: https://github.com/tsenst

visit the speaker at: Github