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 24 25
Introduction
00:00 • 2min
Introduction to Large Language Models
01:41 • 2min
What Is a Large Language Model?
03:24 • 2min
The History of Transfer Learning
04:56 • 3min
The Importance of Large Language Models
08:17 • 2min
How HANA Can Help You Make Better Decisions
10:00 • 2min
How to Integrate Large Language Models Into Your Product
11:56 • 3min
The Open Source Movement
14:31 • 2min
The Benefits of Off-the-Shelf Models
16:50 • 3min
How to Use OpenAI in Production
19:25 • 4min
The Engineering Gap in ML
23:30 • 2min
The Trade-Offs of Learning ML to Do ML
25:25 • 2min
Building Machine Learning Powered Applications
27:25 • 2min
The Future of LLM in Production
29:52 • 2min
The Cost, Quantity and Latency Triangle in Software Development
31:49 • 2min
The Importance of Re-Architecting Production
33:54 • 2min
The Importance of Engineering Skills in MLP
35:57 • 3min
The Moore's Law Approach to Large Models
38:44 • 2min
The Divergence of Real-Time Use Cases
41:06 • 2min
The Cost of Latency in a Condensed Version Event
43:30 • 3min
How to Optimize for Cost in Meta Scale
46:37 • 2min
How to Avert Cost in ML Projects
48:29 • 2min
The Importance of Trust in Language Models
50:05 • 2min
How to Make Probabilistic Workflows Feel More Deterministic
52:27 • 2min
The Benefits of a Constant Look Up for Databases
54:42 • 3min


