DATA festival #Career Partner
Carl Zeiss AG – Scalable Outlier and Anomaly Processing

June 13th, 10.45 – 11.15 am

Time-series data is a common data type at ZEISS, e.g. recorded during manufacturing or in running devices. A dependable outlier and anomaly processing is a crucial requirement to facilitate data-driven use cases such as proactive service enablement or improving first-time yield in production. The inclusion of domain expert knowledge is vital, although it can be time-consuming and often impractical. Hence, a Scalable Outlier and Anomaly Processing tool is currently under development at the ZEISS AI Accelerator. This tool will incorporate an annotation functionality to offer insightful information about possible irregularities in the data, revealing any unusual usage or condition of the devices. Ultimately, this will empower ZEISS experts to make more informed decisions and optimize their operations.


Speaker: Dan-Timon Rudolph, Product Owner Machine Learning | Carl Zeiss AG

Dan-Timon RudolphAs a Product Owner in the ZEISS AI Accelerator, Dan-Timon Rudolph drives the development of an outlier and anomaly processing product with a team of Data Scientists and Machine Learning Engineers. Electrical Engineer by degree, he started his career designing electronics, software and test routines for a wide variety of devices, from sensors to full machines. After several years of experience at different Start-ups, he followed his passion for combining hardware, algorithms and data and joined ZEISS to contribute to its journey towards a data-driven company.