Matthew Burns develops go-to-market strategies for Samtec’s Silicon-to-Silicon solutions. Over the course of 20+ years, he has been a leader in design, applications engineering, technical sales and marketing in the telecommunications, medical and electronic components industries. Mr. Burns holds a B.S. in Electrical Engineering from Penn State University.
Building AI infrastructure for both research and user-facing products, and bridging the gap between
Dylan Curley has spent the majority of his career in AI from a software engineering background. He focuses on large scale AI systems infrastructure, automation, scaling, and reliability. These days Dylan manages a team of SREs (reliability engineers) at Google, who are responsible for the vast majority of AI systems across Alphabet including launching & operating the latest advances in Generative AI (Bard, Workspace, Search, YouTube, etc). He has also spent time working on AI for medical research and astrophysics, and advises startups in AI.
Juergen has 20 years of experience in building complex solutions leveraging advanced analytics, data science, database design, architecture and other cutting-edge technologies. Working in the AI industry since the mid-1990s, Juergen have built solutions for various industries and leveraged various methods over time. He joined Schneider Electric in 2022 as Vice President AI New Value Stream. His focus is to produce industry grade solutions, translating the combination of core algorithms, robotic, cybernetics and human intelligence. Previously, he worked as Senior Data Science Executive in Accenture and hold various roles related to AI and data. He holds a PhD degree in Business intelligence and new marketing methods from The Philipp University of Marburg, MBA in Executive General Management and MA in Applied Computer, bioinformatics and cybernetic from Paris Lodron Universität Salzburg.
In the case study I provide a deep dive into the area of food production (bakery goods) in europe´s largest bakery factory. The case study is about how we successfully implemented Computer Vision to monitor our production process to evaluate 3.5 mio. bakery goods every day. I will show how this leads to a reduction of food waste during production and how this is aligned with environmental/sustainability goals in food production. I will motivate why AI on the edge was identified as being a crucial success factor and which considerations we took as Schwarz group to get started in this area 3 years ago. I will discuss – based on the case study – how we selected and benchmarked hardware chips (Nvidia, Intel, GPU vs CPU…), how we needed to change software engineering to accommodate for Edge AI, how we integrated edge AI models into our cloud-based infrastructure, how we do the deployment and how/why we still use cloud-based GPUs for model training.
