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 • 3min
The Importance of Being on Call for ML Engineering
03:04 • 2min
How to Find the Correct Abstraction for Things
04:48 • 2min
Auto Data Validation for ML
06:58 • 3min
The Importance of Correlation in Data Validation
09:51 • 2min
The Use of Partition Summaries in Data Engineering
12:11 • 3min
The Challenges of Automating Clustering
14:49 • 5min
The Scale Problem in ML Monitoring
19:35 • 2min
The Importance of Auto Data Validation in Machine Learning
22:03 • 2min
How to Monitor Unstructured Data Meaningfully
24:01 • 2min
How to Build a Data Validation System for Images
25:35 • 2min
The Counterintuitive Conclusions of ML Practitioners
27:06 • 2min
How to Prioritize Experimentation in an Organization
28:44 • 3min
How to Define an ML Experiment
31:34 • 2min
The Frustration With Machine Learning
33:16 • 2min
How to Measure Success for Machine Learning Teams
35:10 • 3min
The Importance of Metrics for Success in MLOps
38:19 • 2min
The Importance of Execution in a Software Development Environment
40:10 • 2min
The Future of Machine Learning and Envelopes
42:22 • 5min
The Complexity of Chat GPT and RLHF
47:07 • 2min
The Importance of Guardrails in ML and MLOps
49:02 • 4min
How to Clean Your Data
52:43 • 1min
How to Cheaply Ski
54:12 • 3min