Hardcore Data Science in Practice
What does it take to put data science into practice? Machine Learning courses usually focus mostly on the algorithms at hand, but there are a lot more aspects to consider if you want to „do Data Science“ at a big company like Zalando. In this talk I cover different aspects like how to go from a basic algorithm to a production system, how to set up cross-functional engineering and data science teams, cultural differences between exploratory work from data scientists and system building work from classical engineering, and how to close the gap from a business product to a machine learning problem.
Dr. Mikio Braun is AI Architect for Search & Personalization at Zalando. He joined Zalando three years ago and has worked as started as Delivery Lead for Search & Recommendation and later switched to being a Principal Researcher in the same area. Before joining Zalando he worked as a Machine Learning Researcher at TU Berlin, Fraunhofer Institute FIRST, and University of Bonn on kernel methods, clustering, social media analysis, and applied projects ranging from bioinformatics, brain computer interface, machine learning in control, and deep learning.
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