A Sommelier's Guide to Recommendation Algorithms: Classical and Graph-Based Recommender Systems
Discussion of recommendation engines and the challenges of large datasets, user preferences, and fast, accurate predictions.
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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/
Every attendees must provide a real name with identification document in order to enter the building. Every Registration without Full Name will be deleted. 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.
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Discussion of recommendation engines and the challenges of large datasets, user preferences, and fast, accurate predictions.
A deep dive into building a cost-optimized, multi-tenant platform for generating diverse multimedia content, including text, images, 2D/3D models, and audio. Explore Intelligent GPU Allocation, Decoupled Workloads and Granular Resource Sharing.