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Vector Similarity Search at Scale

Posted Aug 30, 2021 | Views 508
# Vector Embeddings
# Vector Search
# Pinecone
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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Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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Vishnu Rachakonda
Data Scientist @ Firsthand

Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.

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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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