ML Platform Podcast

ML platform teams, features stores, versioning in data pipelines, and where MLOps extends DevOps with Aurimas Griciūnas and Piotr Niedźwiedź

16 snips
Feb 1, 2023
Guest
Aurimas Griciūnas
Guest
Piotr Niedźwiedź
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Episode notes
1
Introduction
00:00 • 4min
2
The First Two Posts That Hit More Likes
03:57 • 2min
3
MLOps Platform Team vs Data Platform Team?
05:47 • 3min
4
Data Platform Team or ML Platform Team?
09:00 • 2min
5
The Right Time to Consider a Platform Team?
10:46 • 1min
6
Data Platform Team or ML Platform Team?
12:14 • 2min
7
MLOps Platform Team and Data Platform Team?
13:48 • 3min
8
MLOps Is an Extension of DevOps, Right?
16:40 • 2min
9
DevOps and Machine Learning
18:39 • 2min
10
Machine Learning Pipelines - What Are They?
20:17 • 5min
11
Data Versioning in MLOps Space
25:19 • 2min
12
What Is a Feature Store?
27:31 • 2min
13
What Is a Feature Store?
29:13 • 5min
14
Query to Feature Store and Version Data Set
34:10 • 2min
15
MLOP Stack Extension - What Is This Extension?
36:18 • 2min
16
How to Train a Machine Learning Model?
38:29 • 4min
17
Model Deployment
42:30 • 3min
18
Is Spark Script Applied on Unseen Data?
45:05 • 3min
19
Is There a Way to Calculate Features?
48:31 • 2min
20
How Do You Keep on Top of Things?
50:28 • 2min
21
MLOps Engineers - Top 3?
52:04 • 3min