Differences Between Shipping Classic Software and Operating ML Models with a Lead MLOps Engineer at TMNL Simon Stiebellehner, and neptune.ai CEO Piotr Niedzwiedz

1

Introduction

00:00 • 6min

2

MLOps Engineer - What's the Job to Be Done?

05:49 • 2min

3

What's the Difference Between AML and AML?

08:09 • 2min

4

How to Hire ML Ops Engineers?

09:52 • 3min

5

What's Missing From a Back and Software Engineer?

12:23 • 3min

6

Data Science

15:43 • 4min

7

Is Machine Learning Different From Software Engineering?

19:18 • 5min

8

Data Quality Tests - Data Monitoring

24:15 • 2min

9

Data Monitoring vs Data Observability?

25:49 • 2min

10

Data Drift Monitoring - Data Monitoring

27:31 • 2min

11

Can You Use Classic Data Monitoring Tools for Model Monitoring?

29:18 • 2min

12

Anomaly Detection on Top of Alerts

31:29 • 2min

13

Vertical Prototyping

33:23 • 4min

14

Vertical Prototyping Is Too Expensive?

37:02 • 2min

15

AWS SageMaker - Is MLOps the Right Approach?

39:13 • 2min

16

Is There a Machine Learning Pipeline?

40:49 • 3min

17

SageMaker Pipelines - Shouldn't Be Connected?

44:12 • 3min

18

NEPT and ASPE Stick

47:10 • 3min

19

Model Registry

50:23 • 2min

20

Is There a Model Registry Solution?

52:08 • 2min

21

How Do You Monitor a Pipeline?

53:43 • 2min

22

Are You a Senior Dev Ops Engineer?

55:48 • 3min

23

Collaboration Between DevOps and MLOps?

59:05 • 3min