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

Coffee Sessions #52 with Dave Bergstein, Vector Similarity Search at Scale.

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// 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 the challenges of deploying vector search to production applications are, 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 productionizing 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.

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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/

Timestamps

[00:00] Intro to Dave Bergstein

[00:55] Dave’s tech background

[04:33] Building software products

[06:05] Building reliable systems

[07:58] System complexity and testing

[08:30] Pinecone intro

[10:47] Vector Search explained

[11:38] Zeus example

[14:14] Vector Search use cases

[16:55] Translation help

[17:52] Notion on Vector Search

[19:13] Common scenario

[20:38] Engineering challenges

[25:05] Live system updates

[26:03] Compute cost challenges

[26:35] Challenge comprehension

[28:00] Security challenges

[30:47] Importance of security

[31:40] From imaging to ML

[33:08] Lessons from building solo

[33:38] Modern ML tooling

[37:12] MLOps audience gap

[39:10] Supporting diverse professionals

[41:44] Openness in platforms

[41:51] Benefits of in-house work

[42:19] Ecosystem interoperability

[43:04] Interoperability

[45:10] Leveraging open ecosystem

[45:40] Vector ecosystem evolution

[47:40] Rise of Pinecone-like firms