Webinar on demand
DataScienceLab (2 of 2) – Anomaly Detection in Performance Energy Grids
hosted by TrendMiner
Duration: 36 minutes
Digitalization & data science are becoming common terms for process manufacturing companies investigating how to best be prepared for meeting their operational and sustainability goals. One part lies in enabling every employee to make data driven decisions. Another in putting data science in the hands of those operational experts without them needing to be a highly trained data scientist.
In this webinar we will show how machine learning model can easily be created and deployed for water demand forecasting, using sensor generated production data and operational data from contextual data sources.
About the Speakers
Sabine Pietruch is a Data Analytics Engineer at TrendMiner. She has a chemical engineering degree. As part of the Customer Success team in the DACH region, she supports TrendMiner users on their analytics journey and strives to close the gap between process engineers and data scientists.
With a degree in biochemical and chemical engineering and starting a new career as a Customer Success Manager for TrendMiner, Daniel Münchrath brings the two worlds of industry and data analytics together. Since joining TrendMiner in 2017, he has been supporting the DACH-division to grow and succeed in its efforts.