Maintainable Code in Data Science

Channel:
United States Coding Tech
Subscribers:
725,000
Published on ● Video Link: https://www.youtube.com/watch?v=KQ0LY_0Pqck


Duration: 1:27:55
6,799 views
141

Notebooks are great, they allow to explore your data and prototype models quickly. But they make it hard to follow good software practices. In this tutorial, we will go through a case study.We will see how to refactor our code as a testable and maintainable Python package with entry-points to tune, train and test our model so it can easily be integrated to a CI/CD flow.

EVENT:

PyData London 2019

SPEAKER:

Kevin Lemagnen

PUBLICATION PERMISSIONS:

PyData provided Coding Tech with the permission to republish PyData videos.



Other Videos By Coding Tech


2019-07-27Going Big: PWAs Come to Desktop and Chrome OS
2019-07-26React Tutorial: Zero to Sixty (Using Hooks)
2019-07-25Firmware Security: Why It Matters and How You Can Have It
2019-07-25Simplicity Is Not Simple
2019-07-24Weaving Webs of Workers
2019-07-23Pandas for Fun and Profit: Using Pandas for Successful Investing
2019-07-23A Deep Dive into NLP with PyTorch
2019-07-22Cloud Made Simple with Serverless Python
2019-07-21Hardware Hacking 101
2019-07-20Libre Not Libra: Facebook's Blockchain Project
2019-07-19Maintainable Code in Data Science
2019-07-18Captain Marvellous JavaScript: A Look at How Hackers Use JS
2019-07-17Design Science (aka Psychology & UX)
2019-07-16Ancient Math and Modern C++
2019-07-15Async/Awaiting Production
2019-07-15http4s: Pure, Typeful, Functional HTTP in Scala
2019-07-13The Future of Conversational Experience
2019-07-12The Internet is Not Safe
2019-07-12Top 10 JavaScript Vulnerabilities (aka OWASP Top 10 for JavaScript Developers)
2019-07-12Hacking Web Performance
2019-07-11Unleash The Power of C++ In Python


Tags:
data science
code
coding
programming
machine learning
python
jupyter notebook