AI & Cloud: Graph-Based Recommenders and Cost-Efficient Generative Architectures
Details
Important! Registration possible ONLY here https://gdg.community.dev/events/details/google-gdg-berlin-presents-ai-amp-cloud-graph-based-recommenders-and-cost-efficient-generative-architectures/
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Join us for a deep dive into two exciting froantiers of AI. We’ll explore how graph-based approaches can enhance recommendation systems, and how to architect scalable, cost-efficient generative AI platforms using Cloud Run, GKE, and Vertex AI. Expect practical insights into building smarter algorithms and running them efficiently in the cloud.
Agenda
17:30 – Doors Open
Arrive early, grab a drink, and meet the community before the talks kick off.
18:00 – Scaling Generative AI: Cost-Efficient Multimedia Platform Architecture with Cloud Run, GKE, and Vertex AI
by Nikolai Danylchyk
This talk is a deep-dive into building a cost-optimized, multi-tenant platform for generating diverse multimedia content, including text, images, 2D/3D models, and audio. We will explore the technical challenges and solutions for balancing high throughput with low operational cost with Intelligent GPU Allocation, Decoupled Workloads and Granular Resource Sharing.
☕️ Break – 10 min
18:50 – A Sommelier's Guide to Recommendation Algorithms: Classical and Graph-Based Recommender Systems
by Moritz Wegener
Recommendation engines are all around us – on Netflix, Spotify, Amazon and many other platforms, they subtly shape what we watch, listen to, or buy. In fact, around 80% of what users watch on Netflix comes from recommendations, making these systems critical for user satisfaction and engagement. But building effective recommendation systems comes with challenges: massive datasets, complex user preferences, and the need for fast, accurate predictions.
☕️ Break – 10 min
19:40 – TBD Talk
Details to be announced — stay tuned!
🕺 Networking until 21:30 💃
Enjoy discussions with the speakers and connect with fellow AI & cloud practitioners.
🍕 Food, snacks, and drinks will be served.