🤹‍♀️ Building models that actually perform with Kyle Gallatin

1

Introduction

00:00 • 2min

2

Data Science - What a Unique Path?

01:44 • 2min

3

Is It a Prerequisite to Getting Into Machine Learning?

03:41 • 3min

4

Machine Learning Platform at Etse

06:27 • 2min

5

What's the Definition of the EME Platform?

08:23 • 2min

6

Is There a Split Between Data Science and Training?

10:18 • 3min

7

Tensorflow

13:14 • 2min

8

The Experimentation Side of Machine Learning?

15:41 • 3min

9

Having a Data Scientist and a Machine Learning Team?

18:36 • 3min

10

Is Machine Learning the First Step?

21:19 • 3min

11

Is It the Platform Team or the Data Scientist?

23:52 • 3min

12

How Do You Preserve Your Knowledge?

26:24 • 2min

13

Is There a Trade-Off Between Performance and the Results?

28:46 • 3min

14

How Do You Build in Machine Learning Profiles?

31:22 • 3min

15

Machine Learning Is Just Like Soft Development

34:10 • 2min

16

Are You Prescriptive With Tensorflow?

36:29 • 2min

17

Machine Learning Infrestructure

38:41 • 3min

18

Scaling Machine Learning - Is Scaling the Right Thing?

42:05 • 2min

19

The Scale of Data, the Scales of Models

44:25 • 2min

20

Lie, Is There a Recurring Theme for Machine Learning?

45:59 • 2min

21

Is It a Good Idea to Work With Cobenets?

47:38 • 3min

22

Doctor in Coubernetes for Machine Learning

51:01 • 3min

23

Are You Curious About Something?

53:58 • 4min