Qdrant AI Builders: Show & Tell @ AWS Loft
โ๐ Join us for an exclusive evening of innovation, networking, and cutting-edge demos at the AWS Loft! ๐
โQdrant, a vector database for AI and semantic search, is bringing together the brightest minds in vector search, AI, and MLOps for a Show & Tell event designed to inspire and educate. Whether you're an AI engineer, data scientist, researcher, or builder, this is your chance to see real-world applications of vector search in action and connect with a vibrant community of industry experts.
โ๐ฅ Expect a curated selection of live demos from companies and individuals who are using Qdrant to build next-generation search, recommendation systems, and AI applications.
โ๐ฅ Network with engineers, founders, and investors passionate about the future of AI and machine learning.
โ๐ฅ Learn how Qdrant powers Agentic Frameworks, Retrieval-Augmented Generation (RAG), multimodal search, and beyond!
โWhat to Expect
โ๐น 5:00 PM โ Doors Open & Networking ๐ค
โ๐น 5:20 PM โ Welcome & Introduction to Qdrant ๐ค
๐น 5:40 PM โ AWS Presentation ๐งโ๐ป
โ๐น 6:00 PM โ Lightning Demos โจ
โ๐น 8:00 PM Networking
โ๐น 9:00 PM โ Event Ends ๐ช
โApply to Demo!
โAre you building something exciting with Qdrant? Do you have an innovative application of vector search, AI-powered retrieval, or semantic search?
โ๐ฉโ๐ป Weโre looking for 8-10 incredible demos to feature at this event. Submit your application by TBD to be considered.
โCriteria:
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Must be an AI/ML application involving search, retrieval, recommendations, discovery, etc.
โ
Must be able to attend the event to present.
โ
Preference for innovative use cases & real-world applications!
โWhy Attend?
โ๐ See cutting-edge AI & search technology in action
๐ Meet top engineers, researchers, and founders in the AI space
๐ก Discover new use cases for Qdrant & vector search
๐ Connect with the SF AI/ML community in a high-energy, interactive setting
โThis exclusive, invite-only event is the perfect opportunity to engage with the AI/ML community, exchange insights, and push the boundaries of what's possible with vector search.