#027 Building the database for AI, Multi-modal AI, Multi-modal Storage

How AI Is Built

Imagine a world where data bottlenecks, slow data loaders, or memory issues on the VM don't hold back machine learning.

Machine learning and AI success depends on the speed you can iterate. LanceDB is here to to enable fast experiments on top of terabytes of unstructured data. It is the database for AI. Dive with us into how LanceDB was built, what went into the decision to use Rust as the main implementation language, the potential of AI on top of LanceDB, and more.

"LanceDB is the database for AI...to manage their data, to do a performant billion scale vector search."

“We're big believers in the composable data systems vision."

"You can insert data into LanceDB using Panda's data frames...to sort of really large 'embed the internet' kind of workflows."

"We wanted to create a new generation of data infrastructure that makes their [AI engineers] lives a lot easier."

"LanceDB offers up to 1,000 times faster performance than Parquet."

Change She:

• LinkedIn
• X (Twitter)

LanceDB:

• X (Twitter)
• GitHub
• Web
• Discord
• VectorDB Recipes

Nicolay Gerold:

• LinkedIn
• X (Twitter)

00:00 Introduction to Multimodal Embeddings
00:26 Challenges in Storage and Serving
02:51 LanceDB: The Solution for Multimodal Data
04:25 Interview with Chang She: Origins and Vision
10:37 Technical Deep Dive: LanceDB and Rust
18:11 Innovations in Data Storage Formats
19:00 Optimizing Performance in Lakehouse Ecosystems
21:22 Future Use Cases for LanceDB
26:04 Building Effective Recommendation Systems
32:10 Exciting Applications and Future Directions