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Ever wondered how AI systems handle images and videos, or how they make lightning-fast recommendations?

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I build AI systems that turn unstructured data into business value. Optimizing at system level, creating feedback loops, and building data assets. CEO @Aisbach | Host How AI Is Built

Discover how you can reduce cost, boost relevance, and enable new capabilities in your vector database. In our latest episode of How AI Is Built, we dive deep into the world of vector databases with Zain Hasan, ML Engineer at Weaviate. Zain shares his expertise on how vector databases are transforming search and recommendation systems. 3 Key Insights: - Reduce costs by up to 80% with vector quantization techniques like binary and product quantization - Boost search relevance by combining vector search with keyword search in a hybrid approach - Enable powerful new capabilities with multi-vector and multimodal search across text, image, audio, and more Check out the full episode now: https://lnkd.in/d_XZHx92 Let me know below: - How are you currently using or planning to use vector databases? - What are the biggest challenges you face with search or recommendation systems? #VectorDatabases #SemanticSearch #RecommenderSystems

Mastering Vector Databases: Product & Binary Quantization, Multi-Vector Search

Mastering Vector Databases: Product & Binary Quantization, Multi-Vector Search

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