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HomeData Engineering and Data WarehousingEasily Build Advanced Similarity Search With The Pinecone Vector Database

Easily Build Advanced Similarity Search With The Pinecone Vector Database

Summary

Machine learning models use vectors as the natural mechanism for representing their internal state. The problem is that in order for the models to integrate with external systems their internal state has to be translated into a lower dimension. To eliminate this impedance mismatch Edo Liberty founded Pinecone to build database that works natively with vectors. In this episode he explains how this technology will allow teams to accelerate the speed of innovation, how vectors make it possible to build more advanced search functionality, and how Pinecone is architected. This is an interesting conversation about how reconsidering the architecture of your systems can unlock impressive new capabilities.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Edo Liberty about Pinecone, a vector database for powering machine learning and similarity search

Interview

Introduction
How did you get involved in the area of data management?
Can you start by describing what Pinecone is and the story behind it?
What are some of the contexts where someone would want to perform a similarity search?
What are the considerations that someone should be aware of when deciding between Pinecone and Solr/Lucene for a search oriented use case?

What are some of the other use cases that Pinecone enables?
In the absence of Pinecone, what kinds of systems and solutions are people building to address those use cases?
Where does Pinecone sit in the lifecycle of data and how does it integrate with the broader data management ecosystem?
What are some of the systems, tools, or frameworks that Pinecone might replace?

How is Pinecone implemented?
How has the architecture evolved since you first began working on it?

What are the most complex or difficult aspects of building Pinecone?
Who is your target user and how does that inform the user experience design and product development priorities?
For someone who wants to start using Pinecone, what is involved in populating it with data building an analysis or service with it?
What are some of the data modeling considerations when building a set of vectors in Pinecone?
What are some of the most interesting, unexpected, or innovative ways that you have seen Pinecone used?
What are the most interesting, unexpected, or challenging lessons that you have learned while building and growing the Pinecone technology and business?
When is Pinecone the wrong choice?
What do you have planned for the future of Pinecone?

Contact Info

Website
LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links

Pinecone
Theoretical Physics
High Dimensional Geometry
AWS Sagemaker
Visual Cortex
Temporal Lobe
Inverted Index
Elasticsearch
Podcast Episode

Solr
Lucene
NMSLib
Johnson-Lindenstrauss Lemma

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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