
AI Engineering Podcast How To Design And Build Machine Learning Systems For Reasonable Scale
Sep 10, 2022
Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Introduction
00:00 • 3min
The Future of Open Source for ML?
03:22 • 4min
How Do I Do the Equivalent of Hello World and the Next Thing?
06:54 • 3min
The Biggest Change in Data Engineering and Data Management
10:08 • 4min
What Are Some of the Mistakes That You've Made?
14:37 • 3min
The Challenges in Machine Learning
18:05 • 3min
ML Engineers
21:31 • 3min
Is the Job of an ML Engineer Enough?
24:50 • 2min
How to Train a Big Data Model?
26:48 • 3min
Data With Training Is Data With Deployment
29:53 • 2min
ML Recommendation Engines - What Are the Bottlenecks?
32:22 • 4min
Scalable Scalability in Machine Learning
35:53 • 3min
How to Become One With Your Data
38:51 • 4min
ML Monitoring
42:22 • 2min
Machine Learning - What I've Learned From a Small Team?
44:34 • 3min
Is Streaming the Next Big Thing in ML?
47:39 • 2min
How to Design and Build a ML System at Reasonable Scale?
49:42 • 2min
The Biggest Barrier to Machine Learning Today
51:49 • 2min
