Data Engineering Podcast

What Does It Really Mean To Do MLOps And What Is The Data Engineer's Role?

Apr 16, 2022
Ask episode
Chapters
Transcript
Episode notes
1
Introduction
00:00 • 3min
2
Mel Community - What's It All About?
02:51 • 2min
3
Why Date Engineers Should Care About M L Ops?
04:45 • 4min
4
Data Engineering
08:43 • 5min
5
What Are the Open and Active Questions in the M Lops Community?
13:44 • 3min
6
The Processes of Machine Learning
16:29 • 4min
7
Machine Learning and Data Science - What Are the Primary Personas in the Devops Community?
20:12 • 4min
8
What Production Means for Machine Learning?
24:15 • 2min
9
When Machine Learning Is Just a Software System?
26:29 • 4min
10
Machine Learning
30:02 • 2min
11
The Fundamentals of a Robust M L Experimentation Tool
32:22 • 5min
12
Do You Need a Dedicated Machine Learning Monitoring Tool?
37:12 • 1min
13
Is Cube Flow a Good Idea?
38:39 • 3min
14
Machine Learning Engineers - The Most Important Thing About Machine Learning
41:21 • 5min
15
Machine Learning and Data Science - What Are the Challenges?
45:54 • 3min
16
Machine Learning - The Interface Between the Data Engineer and the Data Scientist
48:54 • 5min
17
Monte Carlo - The Leading End to End Data Observability Platform
54:11 • 2min
18
Is There a Need for More Efficient Resource Allocation?
55:59 • 5min
19
How to Deal With Vendors?
01:00:52 • 5min
20
What Are Your Predictions for the Future of Machine Learning?
01:06:15 • 4min
21
Data Engineers - What Skills Are Relevant for Data Science?
01:10:33 • 2min
22
What's the Biggest Gap in Data Management?
01:12:47 • 3min