Webinar on demand
Water Demand Forecasting with Machine Learning
hosted by TrendMiner
Duration: 53 minutes (40 minutes presentation + 13 minutes Q&A)
Process manufacturing companies are increasingly exploring the use of digitalization and data science to meet their operational and sustainability objectives. One aspect of this involves empowering all employees to make decisions based on data, while another involves providing operational experts with access to data science tools without requiring extensive training.
In an upcoming webinar, we will demonstrate how machine learning models can be created and deployed to forecast water demand. This will be achieved by utilizing production data generated by sensors, as well as contextual data from other 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.
