Vectoring in on Pinecone

Practical AI: Machine Learning, Data Science, LLM

Daniel & Chris explore the advantages of vector databases with Roie Schwaber-Cohen of Pinecone. Roie starts with a very lucid explanation of why you need a vector database in your machine learning pipeline, and then goes on to discuss Pinecone’s vector database, designed to facilitate efficient storage, retrieval, and management of vector data.

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Sponsors:

• Plumb – Low-code AI pipeline builder that helps you build complex AI pipelines fast. Easily create AI pipelines using their node-based editor. Iterate and deploy faster and more reliably than coding by hand, without sacrificing control.

Featuring:

• Roie Schwaber-Cohen – Twitter, GitHub, LinkedIn
• Chris Benson – Twitter, GitHub, LinkedIn, Website
• Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

• Pinecone
• Pinecone | Blog

Something missing or broken? PRs welcome!

Chapter

1. Welcome to Practical AI (00:00:00)

2. AI Engineer World's Fair (00:00:43)

3. Origins of Pinecone (00:02:18)

4. Vector storing methods (00:04:53)

5. Advantages of vector search (00:07:49)

6. Compressing representation (00:11:25)

7. Sponsor: Plumb (00:13:39)

https://useplumb.com/

8. What is a vector db? (00:15:16)

9. Key functionality (00:18:00)

10. Onboarding enterprises (00:22:32)

11. Internal vs external RAG (00:27:11)

12. Serverless experience (00:28:22)

13. User experience (00:32:15)

14. What is Pinecone enabling? (00:35:44)

15. It shouldn't be complicated (00:38:11)

16. A look into the future (00:40:03)

17. Thanks for joining us! (00:42:52)

18. Outro (00:43:24)