Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Introduction
00:00 • 2min
The Evolution of Data Modeling
01:51 • 4min
The Importance of Graph Databases
05:34 • 2min
The Distinctions Between Operational and Analytics
07:34 • 2min
The Growth of Graph Data Science
10:00 • 3min
The Key Tools in Graph Data Science
13:14 • 2min
The Popularity of Graph Neural Networks in Data Science
15:03 • 2min
The Problems With GNNs in Production
16:42 • 3min
The Evolution of Graph Intelligence
19:28 • 6min
The Future of Vector Databases
24:58 • 5min
The Importance of Vector Databases in Deep Learning
29:44 • 3min
The Future of Graph Databases
32:33 • 3min
Embeddings as Part of an Operational System
35:40 • 2min
The Power of Knowledge Graphs
37:45 • 2min
The Future of Knowledge Graphs
40:08 • 2min
The Future of Knowledge Graphs
41:53 • 4min
The High Order Bit of Democratizing Access
45:43 • 2min
The Importance of Custom Foundation Models
47:44 • 2min
The Future of Custom Foundation Models
50:02 • 2min
GPUs for Graph Related Data
51:59 • 2min
Neo4j: How to Build a Community With No Moat
53:50 • 3min
The Secret Sauce of Vector Search
56:44 • 2min
How to Create Mode in a No-Mode World
58:24 • 3min