Workshop “Neue Ansätze und Anwendungen der KI” (04.07.2024)

Zeit: Donnerstag, den 04.07.2024 Ort: TPL (0.018) im Erdgeschoß des Informatikgebäudes der Universität Stuttgart, Universitätsstraße 38 Programm ab 13:00 Eintreffen der Teilnehmer 13:15 – 13:30 Begrüßung, Agenda & Vorstellungsrunde (B. Mitschang, Universität Stuttgart) 13:30 – 14:00 GenAI-Applikationen bauen – und ihren Ergebnissen vertrauen – mit IBM watsonx (A. Lang, IBM) 14:00 – 14:30 Multimodal ADAS…

Continue reading →

IBM on Campus – Data and AI (11.12.2023)

Dear students, we are happy to announce the next IBM on campus event on data and AI. Generative AI based on large language models is inherently data-driven; but how can we build and maintain real-work applications when the underlying data changes? Re-training models is slow and expensive. We’re going to explore faster alternatives based on…

Continue reading →

Practical Cloud Topics – Platforms, Services and Best Practices (WS2023/24)

Prof. Dr. Kristof Kloeckner (CTO IBM GTS, retired) Gerd Breiter (IBM Distinguished Engineer, retired) Webinar Series. 1.5 hrs/week on Thursday at 15:45, starting on October 19. Exercises (Übungen) will be held online each Tuesday at 17:30, starting on October 31 Join the webinar at the specified dates and times here: https://unistuttgart.webex.com/meet/kristof.kloeckner Registration for the lecture…

Continue reading →

Practical Cloud Topics – Platforms, Services and Best Practices (WS2022/23)

Prof. Dr. Kristof Kloeckner (CTO IBM GTS, retired) Gerd Breiter (IBM Distinguished Engineer, retired) Webinar Series. 1.5 hrs/week on Thursday at 15:45, starting in the week of October 17. https://unistuttgart.webex.com/meet/kristof.kloeckner Registration (only for non-students! Please refer to ILIAS otherwise): The course will focus on ‘Practical Cloud Topics – Platforms, Services and Best Practices’, with real-life…

Continue reading →

Designing a comprehensive Data Lake Architecture (DLArchitecture)

Initiatives like Industry 4.0 generate large amounts of heterogeneous data that need to be stored and managed. It is not always clear what benefits this data will later bring to the company. As a result, it is usually not possible to decide at the time of data collection what value the data will have. To avoid losing any potentially important information, all data are stored in their raw format in an enterprise-wide data lake. The goal of this project was to define a framework for an implementable data lake architecture.

Continue reading →