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.

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Further reading about Data Science

☞ Machine Learning A-Z™: Hands-On Python & R In Data Science

☞ Python for Data Science and Machine Learning Bootcamp

☞ Machine Learning, Data Science and Deep Learning with Python

☞ Deep Learning A-Z™: Hands-On Artificial Neural Networks

☞ Artificial Intelligence A-Z™: Learn How To Build An AI

☞ A Complete Machine Learning Project Walk-Through in Python

☞ Machine Learning In Node.js With TensorFlow.js

☞ Top Python IDEs for Data Science in 2019

☞ A “Data Science for Good“ Machine Learning Project Walk-Through in Python

☞ Beginner’s Guide to Jupyter Notebooks for Data Science

☞ Python Certification Training for Data Science

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