Catch up on the latest talks from Munich Datageeks โ fresh insights, cutting-edge tech, and real-world data stories from top minds in the field. Stay curious and stay inspired!
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Fine-Tuning Language Models: When to customise Language Models for better performance by Laurens Tetzlaff from Netlight presented at Munich Datageeks - September Edition 2025. Abstract This talk covers how LLMs changed the way we do and see Data Science, a decision matrix on when to consider what type of GenAI
Backpropagation Boys: How to burn money with AI-generated music by Torsten Schรถn was presented at Munich Datageeks - September Edition 2025 Abstract This talk is about how someone with absolutely no musical talent managed to release eight albums within a year. From a first attempt and a somewhat crazy idea,
๐๐ฎ๐ง๐ข๐๐ก ๐๐๐ญ๐๐๐๐๐ค๐ฌ โ ๐๐ฎ๐ฅ๐ฒ ๐๐๐๐ญ๐ฎ๐ฉ ๐๐๐ข๐ญ๐ข๐จ๐ง ๐โฃโฃ What an evening! Our July Meetup at Celonis brought together passionate data enthusiasts, innovative minds, and engaging discussions in a fantastic setting. A huge thank you to everyone who joined us!โฃโฃ 1๏ธโฃ Dr. Pol Schumacher kicked off the evening with "How to mine better models, fast!"โฃ He guided
When MFA Isn't Enough: Dissecting a Sophisticated Phishing Campaign Targeting University Infrastructure by Marleen Steinhoff was presented at Munich Datageeks - June Edition 2025 Abstract This presentation analyses a recent security breach at our university, showing how attackers bypassed Multi-Factor Authentication (MFA) to access Microsoft 365 accounts. We&
Apache Iceberg and the Future of Secure, Interoperable Data Lakehouses by Christian Thiel was presented at Munich Datageeks - May Edition 2025 Abstract Apache Iceberg has emerged as the de-facto standard for open Data Analytics systems, empowering organizations to freely choose their preferred query engines and ML frameworks while working
The Future is Fast, Open, and Deployable Anywhere: When Fire(bolt) meets Ice(berg) by Georg Kreuzmayr was presented at Munich Datageeks - May Edition 2025 Abstract Analytics and AI-driven applications demand both scalable and low-latency data access. However, data infrastructure capable of meeting these requirements often suffers from prohibitive
Forecasting and optimization to balance a renewables-powered electricity grid using batteries by Danil Vityazev & Wolfgang Ridinger was presented at Munich Datageeks - April Edition 2025 Abstract Large-scale battery systems are becoming an increasingly important feature of the electricity grid as they balance intermittent production from renewables with demand for
Efficient Medical Data Extraction: Automating ICD-10 Code Identification from Documents by Alexander Hoeweler was presented at Munich Datageeks - March Edition 2025 Abstract In this talk, we will explore how we automate the extraction of complex medical data from documents, focusing on the identification of ICD-10 codes. We will also
Using uncertainty estimation to build reliable ML systems for smart manufacturing by Lukas Lodes was presented at Munich Datageeks - March Edition 2025 Abstract Modern production lines generate large amounts of data that can be used to increase the efficiency of both equipment and materials. This includes, of course, predictive
Scaling from POC to Production by Nicolas Neudeck was presented at Munich Datageeks - February Edition 2025 Abstract Turning an AI-powered proof of concept into a scalable, production-ready product is significantly more complex than simply rolling out the existing solution. POCs often lack robustness, scalability, and long-term maintainability. This talk
Massively Multimodal Input Management in CIB flow by Konrad Grosser was presented at Munich Datageeks - January Edition 2025 Abstract Documents are rich in both linguistic and visual information, playing a critical role in many business processes. The automatic understanding of such documents is an evolving research field, requiring robust
Adversarial Attacks in Deep Reinfocement Learning: A Call for Robust Defenses by Adithya Mohan was presented at Munich Datageeks - January Edition 2025 Abstract Deep Reinforcement Learning (DRL) has demonstrated remarkable potential across domains, including robotics, autonomous driving, and gaming. However, its vulnerability to adversarial attacks poses significant challenges to