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
Data Science, Code Quality Hierarchy of Needs
02:16 • 2min
What Is the Hierarchy of Needs?
04:23 • 2min
The Most Important Concept in Clean Code and Code Quality
06:16 • 3min
Data Scientists - What Is the Definition of Technical Debt?
09:14 • 2min
Machine Learning - Technical Debt and Technical Mess
10:57 • 3min
Code Refactoring
13:32 • 4min
Code Reviews for ML Projects?
17:37 • 2min
Using Layers of Abstraction in Python
19:51 • 4min
How to Release New Better Machine Learning Models Without Much Downtime?
23:41 • 3min
Refactoring and Clean Code Enables DevOps
26:27 • 4min
Data Scientists - Is Blue Code a Red Flag?
30:52 • 4min
Developing a Testable Architecture for a ML Project
35:17 • 2min
What Are Your Best Resources for Data Scientists to Learn How to Write Clean Code?
37:17 • 4min
How to Build a Microservice?
41:41 • 2min
Data Drift Monitoring
43:30 • 3min
How Do You Structure ML Projects to Reduce Technical Debts?
46:25 • 2min
MLOps Live - Part 2
48:30 • 2min


