This survey aims to gather a detailed understanding of the state of machine learning adoption and usage in companies, as well as the challenges and opportunities they face in this area.
We focus on where and how organisations successfully deploy machine learning solutions to generate impact. For instance, to lower costs, increase customer satisfaction or generate additional revenue.
Participate in our research project by registering for the survey todayThe survey is conducted between Zühlke, a leading technology consulting firm based in Zürich, and ETH Zürich, one of the top universities in Europe for computer science and engineering.
Your participation in this survey will greatly contribute to the development of machine learning strategies and solutions for companies. The results will also identify areas for future research and collaboration. We greatly appreciate your participation in this survey and as a thank you, we will give you exclusive access to the survey's findings before it is officially published.
All responses to this survey will be kept strictly confidential and used only for research purposes. Your responses will be aggregated and analyzed in a way that does not reveal the identity of your firm or any individual respondents. The anonymity and integrity of your personal information will be preserved at all times and are assured through the data protection guidelines of ETH Zürich.
We are dedicated to ensuring your survey experience is as smooth as possible. Our team of experts will be on hand to provide assistance in person or by phone during your scheduled survey time.
We truly appreciate your participation in this important research as your input will play a vital role in shaping the future of machine learning in companies.
Gabriel Krummenacher leads the Data Science Team at Zühlke and has several years of experience in conducting data analytics and machine learning projects. His main focus is on medical machine learning applications and bringing prototypes to production. He holds a PhD and M.Sc. from the Institute for Machine Learning at ETH, where he worked on scalable methods for large-scale and robust learning, wheel defect detection and sleep stage prediction with deep learning.
