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EP.260 Vector Search Secrets Revealed! - AI-Powered Image Search with MongoDB - Live Demo
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A tartalmat a MongoDB biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a MongoDB vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
Ever wondered how companies like Amazon or Pinterest deliver lightning-fast image search? Dive into this episode of MongoDB Podcast Live with Shane McAllister and Nenad, a MongoDB Champion, as they unravel the magic of semantic image search powered by MongoDB Atlas Vector Search!
🔍 What You’ll Learn:
Why semantic search is a game-changer for AI-driven applications (spoiler: it’s all about meaning, not just keywords!).
How to transform images into vectors (embeddings) and store them alongside your data—no clunky ETL pipelines required!
A live demo showcasing real-time image search on a 40,000+ product dataset (spoiler #2: it’s blazing fast and shockingly simple to build!).
Pro tips on choosing embedding models, optimizing indexes, and avoiding common pitfalls.
Why MongoDB’s unified data model crushes traditional databases for AI/ML workloads.
👥 For Developers & Data Engineers:
Whether you’re a JavaScript, AI enthusiast, or just tired of JOINs, this episode is your roadmap to building smarter, faster apps. Plus, learn why companies like Toyota and Verizon trust MongoDB for mission-critical AI workloads.
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