Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Introduction
00:00 • 2min
Mark Sheebold: Data Analyst at Right Brain Networks
01:36 • 2min
How Data Ops Came to Be
03:09 • 2min
The Paradigm Shift of Data Ops
04:45 • 3min
How to Mask Your Data to Make It More Real
07:29 • 2min
What Is Data Ops?
09:04 • 2min
How to Automate Machine Learning Jobs and Procedures
10:42 • 2min
How to Use Format Preserving Encryption for Masking Data
12:37 • 2min
AWS and the DevOps Environment
14:19 • 2min
How General Motors Works
16:19 • 2min
The Importance of Team Topologies in Data Management
17:56 • 2min
GM's Application Lifecycle Tools
19:46 • 2min
How to Use Data to Measure Success
21:40 • 2min
The Challenges of Aligning Data Across Teams
23:55 • 2min
How to Make Your Schema Easy to Digest
25:31 • 2min
Best Practices for Data Ops
27:11 • 2min
The Importance of Data in AI Modeling
28:45 • 1min
The Challenges of Machine Learning Grants
30:15 • 2min
Best Practices for Data Management Processes
32:16 • 3min
GM's Internal System for Compliance
34:49 • 3min