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In this PyData London talk,  Kevin Lemagnen covers something that I’ve long wondered about: the maintainability of code created in data science projects.

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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