The potential of Machine Learning and AI models is a common topic in the media, yet their implementation in practice often doesn't live up to the hype.
Why is this?
In this keynote, we will address this question heads-on, investigating the various factors influencing the return on investment when implementing Machine Learning and AI models in real-world scenarios. We will explore this topic from several viewpoints: use case requirements, compute, benchmarking procedures, and machine learning operations. Drawing from real-life examples within the Smart Infrastructure sector at Siemens, we will show where and how Machine Learning can yield significant improvements.
Join us as we clarify misconceptions, challenge assumptions, and reveal the genuine potential of Machine Learning models in practice.
SPEAKER
Dr. Edouard Fouché is an expert in Data Science and has extensive experience in building efficient algorithms to address decision-making problems in energy systems. Edouard works at Siemens Smart Infrastructure Chief Technology Office in Nürnberg as a Data Scientist. He acts as a key developer and operational architect. In 2020, he graduated Summa Cum Laude with his Ph.D. in Data Science at KIT. He received the Helmholtz Doctoral Prize for his contributions to extracting knowledge from large data streams. After that, he became a group leader and the principal lecturer for Data Science at KIT.
TICKETS
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LANGUAGE
English