The AI-Native Multimodal Lakehouse | LanceDB скачать в хорошем качестве

The AI-Native Multimodal Lakehouse | LanceDB

What if the bottleneck in AI isn’t the model... it's retrieval speed? In our latest interview, our Head of AI Bryan Bischof sits down with LanceDB CEO & Co-founder Chang She to unpack why “almost every problem becomes a search problem” because legacy stacks weren’t built for multimodal + agentic workloads 0:00 Riding in a Waymo + quick intros 0:45 What LanceDB is and why search sits under AI 1:49 What “multimodal” really means for enterprise data 3:12 The research-to-production gap (offline vs online) 3:45 Retrieval as the real AI differentiator 5:01 Why LanceDB works for retrieval 6:10 A different vector search architecture (cost + scale) 7:28 Multiple indexes: experimentation and true multimodal search 10:05 One big embedding vs many specialized ones 11:52 Why pgvector/Postgres breaks down at scale 14:10 Prefiltering, permissions, and agents changing search 17:48 The future of vector search in an agent world 21:00 Quick-fire wrap-up