Lean AI – How to Implement a Production-Ready Deep Learning Model in Twelve Weeks


As traditional media industries are declining, Axel Springer turns to advanced technologies to keep their position as one of the leading publishing companies in Europe. In this talk, Dat Tran, Head of AI at Axel Springer AI, explains how the media giant uses lean approaches to become an AI first company.

Referring to his experiences at Axel Springer’s marketplace idealo.de, Dat will show you how they built and implemented a production-ready Deep Learning model to rank images according to their aesthetics within just twelve weeks. Learn, how to use existing infrastructures like AWS to train machine learning applications and how to successfully maintain them, while not stepping into common pitfalls of AI research and development.


Dat Tran

Head of AI at Axel Springer AI. Held Data Scientist roles at Idealo, Pivotal and Accenture.

Dat Tran is all about devising realistic data-driven use cases to the actual implementation into a real product. He started his career as data scientist at Accenture and Pivotal, where he helped dozens of clients to enter the world of machine learning. He then moved to idealo and Axel Springer, where he wants to make AI accessible to the whole group.