Dependency Inversion Principle for ML Engineers

preview_player
Показать описание
In the last video of the “SOLID for ML Engineers” series, you can learn about the Dependency Inversion Principle, , how to spot its violations, and how you can refactor ML code to respect it.

By satisfying the Dependency Inversion Principle, your ML code will protect your components from the most unstable elements of your code, decrease coupling, and facilitate testing.

Slides:

Code:

===============================

Interested in hiring me as a consultant/freelancer?

Join The Sound Of AI Slack community:

Connect with Valerio on Linkedin:

Follow Valerio on Facebook:

Follow Valerio on Twitter:

===============================

Content:
0:00 Intro
0:17 Defining Dependency Inversion
0:57 How to spot DIP violations
1:30 Code example
2:56 DIP violations in the code example
3:46 Code example design
5:12 How to enforce DIP
6:20 Benefits of DIP
8:10 Refactoring the code
12:34 Final considerations
Рекомендации по теме
Комментарии
Автор

Good job Valerio! Crystal clear as always :) Thanks!

xavierrispal
Автор

Hi Valerio! Awesome playlist, thanks for this content. I may have to put it into practice, but I don't understand the difference between the open closure principle and the dependency reversal principle. If you can recommend me some bibliography or article I would appreciate it.

nahuelpassano
Автор

One question: is it common to use OOP in machine learning?
Both in research and developing the software.
Thanks

MagnusAnand