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Vector Similarity Search at Scale

Posted Aug 30
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SPEAKER
Dave Bergstein
Dave Bergstein
Dave Bergstein
Director of Product @ Pinecone

Dave Bergstein is Director of Product at Pinecone. Dave previously held senior product roles at Tesseract Health and MathWorks where he was deeply involved with productionalizing AI. Dave holds a PhD in Electrical Engineering from Boston University studying photonics. When not helping customers solve their AI challenges, Dave enjoys walking his dog Zeus and crossfit.

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Dave Bergstein is Director of Product at Pinecone. Dave previously held senior product roles at Tesseract Health and MathWorks where he was deeply involved with productionalizing AI. Dave holds a PhD in Electrical Engineering from Boston University studying photonics. When not helping customers solve their AI challenges, Dave enjoys walking his dog Zeus and crossfit.

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SUMMARY

Ever wonder how Facebook and Spotify now seem to know you better than your friends? Or why the search feature in some products really “gets” you while in other products it feels stuck in the '90s? The difference is vector search— a method of indexing and searching through large volumes of vector embeddings to find more relevant search results and recommendations. Dave Bergstein, the Director of Product at Pinecone, joins us to describe how vector search is used by companies today, what are the challenges of deploying vector search to production applications, and how teams can overcome those challenges even without the engineering resources of Facebook or Spotify.

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