How to Structure Your Code for Machine Learning Development?
The highly overlooked yet critical skill for data scientists.
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.
Subscribe to Damien's The AiEdge newsletter for more. You can also follow him on LinkedIn and Twitter.
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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