How to Structure Your Code for Machine Learning Development?

Do you know one of the biggest hurdles data science and machine learning teams face?

It is transitioning their data-driven pipeline from Jupyter Notebooks to an executable, reproducible, error-free, and organized pipeline.

And this is not something data scientists are particularly fond of doing.

Yet, this is an immensely critical skill that many overlook.

Machine learning deserves the rigor of any software engineering field. Training codes should always be reusable, modular, scalable, testable, maintainable, and well-documented.

To help you develop that critical skill, I'm excited to bring you a special guest post by Damien Benveniste. He is the author of The AiEdge newsletter and was a Machine Learning Tech Lead at Meta.

In today’s machine learning deep dive, he shares his template to develop quality code for machine learning development: How to Structure Your Code for Machine Learning Development.

More specifically, the deep dive covers:

• What does coding mean?

• Designing:

  • System design

  • Deployment process

  • Class diagram

• The code structure:

  • Directory structure

  • Setting up the virtual environment

  • The code skeleton

  • The applications

  • Implementing the training pipeline

  • Saving the model binary

• Improving the code readability:

  • Docstrings

  • Type hinting

• Packaging the project

• Takeaways

👉 Interested folks can it here: How to Structure Your Code for Machine Learning Development.

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