Carl Zeiss AG – Data (R)evolution in Service Applications – why predictive maintenance often fails

June 13th, 11.45 – 12.15 pm

ZEISS hardware generates high-quality data. However, it is not always the data required for machine learning. What if we want to improve the service of our products through data? Typical device logs are often insufficient or do not provide the relevant information. The silver lining is that exploratory data analysis coupled with data visualization on the existing data can yield invaluable insights and even spark a data (r)evolution.


Dr. Christina Littlejohn, Data Scientist | Carl Zeiss AG

Zeiss_Dr.-Christina-Littlejohn_CFP-squareAs a Data Scientist in the ZEISS AI Accelerator, Dr. Christina Littlejohn actively collaborates with different ZEISS business segments to develop specialised time series prediction and classification products. With a background in Renewable Energy Technology and Theoretical Economics, Christina has passion for deploying algorithms to realise a more equitable and sustainable society. Prior to joining ZEISS, Christina worked as a Junior Economist at ifo Institute modelling electromobility policy.