Where ML and DevOps Meet

1

Introduction

00:00 • 2min

2

Topendevs Resume - How to Update Your Resume

01:49 • 6min

3

The Difference Between ML and Software?

07:31 • 2min

4

Is Software a Solved Problem?

09:23 • 4min

5

DevOps - Data as Code?

13:25 • 2min

6

Data Validation

15:54 • 2min

7

Is the Scalability of the Data Increasing?

17:40 • 3min

8

DevOps

20:52 • 3min

9

The Most Popular Method in Bioinformatics

23:33 • 3min

10

Python Stats Models - Is NumPy Compilable?

26:56 • 2min

11

Docker Deep Dive Book Club

28:48 • 2min

12

How to Optimize a Workflow in DevOps

30:22 • 4min

13

DevOps

34:14 • 2min

14

Do We Really Need a Factory Pattern?

36:17 • 2min

15

The Importance of Readability in ML and Backend Software Development

38:32 • 3min

16

The Only Time You Shouldn't Be Writing in Line Comments and Code

41:16 • 2min

17

Pandas, I'm Looking at You!

43:30 • 2min

18

Is There Something Wrong With Pandas?

45:21 • 4min

19

Databricks - How to Scale a DataBricks Cluster

49:17 • 2min

20

Is Adaptive Scaling Possible?

51:13 • 3min

21

Databricks Book Club - Part 2

54:14 • 2min

22

The State of Bricks

56:44 • 2min

23

HPC in the Cloud?

58:35 • 2min

24

Is Quantum Computing Adaptable to HPC?

01:00:14 • 4min

25

Is AI Going to Take Our Jobs?

01:03:59 • 2min

26

Is AI Going to Take Over Your Jobs?

01:05:34 • 3min

Where ML and DevOps Meet - ML 108