Vector Search Over Complex Semi-Structured Data

Have you built your first RAG yet? What about turning all your user data into vector embeddings for “similar customer” grouping in analytics? Or are you more of a personalized semantic search fan? Recommender systems? Fraud analysis? Molecule fingerprints?! You have seen the demos, but what does it actually take to launch vector-powered systems into production? And no, the answer is not “add a ton of heuristic filters” nor “build a complex ranking model on top of it” and definitely not “fine-tune an LLM until it starts to work on your click events”. You need a way to combine large pre-trained models that process the unstructured parts of your data like text and images with models trained on your structured data - clicks, relationships, timestamps and beyond. You need a way to express your objective when you formulate the search vector. And most importantly - you need to build feedback loops. Let’s talk about the design of production-grade vector-powered systems that are easy to control, easy to deploy and powerful enough to give your users what they really really want.. and how Superlinked makes Vector Search more accessible to everybody in the Databricks ecosystem.
Talk By: Daniel Svonava, CEO, Superlinked
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Big Book of MLOps: dbricks.co/3r0...
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[@KP-ix3ts]

Great video! I can’t wait to apply this to my work

[@superlinked]

🤝