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 • 2min
Building an MLOps Culture
02:14 • 2min
What Makes a Good MLOps Culture?
04:05 • 4min
MLOps - Reproducibility
08:25 • 2min
Agile Is the Best Way to De-Risk the MLOps Culture
10:35 • 6min
How to Shape the MLOps Culture?
16:21 • 2min
MLOps - What's Your Current Tool Stack?
18:45 • 3min
Build vs Buy?
21:19 • 2min
MLOps Principles That Are Tool Agnostic?
23:28 • 2min
How Long Should We Preserve Old Data?
25:08 • 2min
Building an MLOps Framework, What Components Would You Prioritise?
27:16 • 2min
Data Scientists or ML Developers - Do They Want to Learn More?
29:44 • 2min
Deploy ML Models in an Agile Way?
32:00 • 5min
How to Shape Our MLOps Culture to Only Focus on the Most Important Things That Solve Our Problem?
36:58 • 2min
The Technical Aspect of a Solid MLOps Culture
38:48 • 4min
Machine Learning Patterns - The First Touch or Four Piece?
42:55 • 5min
What's the Most Cumbersome Part of the MLOps Task?
47:42 • 2min
The Thinnest MLOps Layer Principle
49:37 • 3min


