Munich Datageeks August Edition

We are thrilled to announce our next Meetup on August 29th at IBM.
Format:

• 2 talks (each ca. 40 min incl. discussion)
• Time for networking + food + drinks before, in between and after the presentations
• Talks are held in English
• We will be taking photos and/or film footage at the event. These will be used to share news about our meetups and to publicize upcoming events.

The lineup:
First talk:
Maximilian Burkardt - Using Large Language Models in production – An enterprise approach based on IBM watsonx

Abstract:
Your company is building POC's and MVP's based on Generative AI? To bring your great deliverables successful into production, you need to care about topics like Risk Management, Asset and Metadata tracking as well as Model evaluation/monitoring. Without an enterprise platform, you would need an extensive amount of resources (especially time and skilled people) to build such a solution from scratch. You will learn how to learn how to use our enterprise solution IBM watsonx Governance to manage the end-to-end lifecycle of an AI application in a trustful and sustainable way.

Bio:
Maximilian is an Advisory Technical Sales Specialist for AI based in Germany and his favourite topics are Artificial Intelligence in combination with Business Intelligence (AI+BI) as well as the operationalization of Data Science and AI (MLOps, LLMOps). He has a technical background in Data Science und Business Intelligence as well as 7 years of industry experience in the Retail and Supply Chain Industry (Grocery Retailer) before IBM. In the Retail industry, he worked intensively with domain expert business units on various Data Science use cases and developed an MLOps architecture. At IBM, Max is a trusted Technical Sales Specialist for clients, a Speaker about Data Science, Business Intelligence and Generative AI as well as develops innovative prototypes and demos in the field of AI+BI for a worldwide audience.

Second Talk:
Jonathan Pirnay - Neural Combinatorial Optimization: Methods and Challenges

Abstract:
Combinatorial optimization problems are at the heart of many real-world applications, from logistics and manufacturing to finance, genomics, and beyond. These problems involve finding the best solution from a finite set of possibilities, often under complex constraints. Traditionally, they are solved using methods like exact algorithms, integer programming, and heuristic approaches – all of which require a significant amount of expert knowledge and intuition. In this talk, we will explore the relatively young field of Neural Combinatorial Optimization, which uses deep learning to learn the necessary heuristics from scratch. We will introduce the field, its methods, and especially talk about the challenges it faces.

Bio:
Jonathan is a third year PhD student at the Professorship of Bioinformatics at the TUM Campus Straubing for Biotechnology and Sustainability. Before starting his PhD, Jonathan worked for two years as an AI consultant at Fujitsu, mainly in the area of computer vision. He
received his B.Sc. and M.Sc. in Mathematics from the University of Regensburg. His research focuses on building bridges between reinforcement learning and self-supervised learning to solve complex planning problems.