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Vector Similarity Search at Scale // Dave Bergstein // MLOps Coffee Sessions #52

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Manage episode 313294427 series 3241972
Content provided by Demetrios Brinkmann. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Demetrios Brinkmann or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Coffee Sessions #52 with Dave Bergstein, Vector Similarity Search at Scale.
//Abstract
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.
// Bio 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 Ph.D. in Electrical Engineering from Boston University studying photonics. When not helping customers solve their AI challenges, Dave enjoys walking his dog Zeus and CrossFit.
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Dave on LinkedIn: https://www.linkedin.com/company/pinecone-io/mycompany/

  continue reading

326 episodes

Artwork
iconShare
 
Manage episode 313294427 series 3241972
Content provided by Demetrios Brinkmann. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Demetrios Brinkmann or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Coffee Sessions #52 with Dave Bergstein, Vector Similarity Search at Scale.
//Abstract
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.
// Bio 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 Ph.D. in Electrical Engineering from Boston University studying photonics. When not helping customers solve their AI challenges, Dave enjoys walking his dog Zeus and CrossFit.
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Dave on LinkedIn: https://www.linkedin.com/company/pinecone-io/mycompany/

  continue reading

326 episodes

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