#337: Kedro for Maintainable Data Science

1

Introduction

00:00 • 2min

2

I've Been There From the Beginning

01:49 • 2min

3

The Difference Between Mechanical Engineering and Python Developer Experience

03:56 • 3min

4

Getting the Most Out of Your Code Base

07:18 • 2min

5

The Workflow of a Data Scientist

09:24 • 2min

6

Is There a Space for a Coat Base?

11:18 • 3min

7

The Reproducibility, Maintain Ability Side of Data Science

13:56 • 4min

8

Learn More About Better Adaptive Auto Completion

18:03 • 3min

9

Etha Canter Is a Very Different Focusd

20:56 • 4min

10

Docker - Is There a Templet to Get Started?

25:15 • 4min

11

KEDRO Catalogue

29:32 • 2min

12

Using Python Pipe Lines in Python Scripting

31:27 • 5min

13

Using Pipe Lines in Web Frameworks

35:58 • 2min

14

The Pipe Line Visualization Tool

38:11 • 2min

15

Is This Just for Visualizing the Static Structure?

39:53 • 1min

16

The Pipeline Deployment Plugins

41:23 • 2min

17

Is There a Guide to Deployment?

43:23 • 6min

18

How to Predict the Price of a Space Flight

49:21 • 2min

19

Aqalinkd Ot the Datar Engineer Tutorial

51:09 • 5min

20

The Next Big Thing That Will Be Coming Out of Hedro?

55:56 • 2min

21

Getting Started With KDE

58:13 • 2min

22

Data Science and Data Enginearing - We're in the Open for Community

59:45 • 3min