Sessions 2025
Machine Learning World Europe
17 - 18 November 2025 | Berlin
Check out the first sessions below
17 - 18 November 2025 | Berlin
Check out the first sessions below
Speaker/s:
Dr. Max Schüssler, Krombacher BrauereiFrom Gut Feeling to Forecast Excellence: How Krombacher Achieved Industry-Leading Demand Forecasting
Summary:
How to predict demand for Krombacher in the coming days? Discover how Krombacher built a state-of-the-art forecasting service that improves bottling processes, boosts planning reliability, and eases pressure on production control. Join Max on Krombacher’s journey—what worked, what didn’t, and how data science and technological innovation are driving smarter decisions in the brewing industry.
Speaker/s:
Olga Khryapchenkova, NIO“A Global Voice Assistant for E-Cars: How NIO Leverages Machine Learning for Enhanced User Experience
Summary:
This talk will highlight how the electric car manufacturer NIO leverages machine learning to develop NOMI, the world’s first in-vehicle artificial intelligence. Olga will share lessons learned from integrating LLMs into the existing voice assistant architecture, including the challenges and opportunities this integration has presented for both the product and the organization.
Forecasting Public Expenditure (BAföG) Using LSTMs: A Multi-Task Learning Approach for Volatile Data
Summary:
Accurately forecasting expenditures is crucial for budgeting and policy planning. This session presents a multi-task LSTM model designed to predict annual federal student aid (BAföG) expenditures for the current and next two years. Using 16 state-level time series, it addresses extreme intra-year volatility, strong seasonality, and the risk of overfitting with small datasets. Attendees will gain insights into overcoming these challenges and improving real-world time series forecasting models.
Speaker/s:
Renata Pocitarenco, Johnson & JohnsonModel Predictive Control for Automated Biologics Drug Product Manufacturing at Johnson & Johnson
Summary:
The presentation will focus on the justification, development and implementation of an advanced control strategy for automating the dilution process in fill-finish manufacturing. The novel strategy includes the implementation of sensors, real-time data processing and modeling, combined with immediate process feedback control. This approach was implemented at Johnson & Johnson in the commercial facilities and allows to reach the target protein concentration with high accuracy despite process irregularities.