How to Structure and Test Your Code for ML 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.

We covered a template to develop quality code for machine learning development here: How to Structure Your Code for Machine Learning Development.

Moreover, once you have developed the pipeline, you must also test it, which we covered in detail here: Develop an Elegant Testing Framework For Python Using Pytest.

Why care?

Machine learning deserves the rigor of any software engineering field.

Training codes should always be reusable, modular, scalable, testable, maintainable, and well-documented.

But this is not something data scientists are particularly fond of doing and it is an immensely critical skill that many overlook.

In the machine learning development deep dive (which was a guest post by Damien Benveniste from The AiEdge Newsletter), we covered:

• 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

And in the testing deep dive, we covered the following:

• Why are automation frameworks important?

• How it simplifies pipeline testing?

• How to write and execute tests with Pytest?

• How to customize Pytest’s test search?

• How to create an organized testing suite using Pytest markers?

• How to use fixtures to make your testing suite concise and reliable?

• and more.

Read them here:

• How to Structure Your Code for Machine Learning Development.

• Develop an Elegant Testing Framework For Python Using Pytest.

If you face a hard time writing scripts, if you don’t understand how init files work, how to organize directories, how to ensure that the code meets industry standards, but want to learn them, then these articles are for you.

For those who want to build a career in DS/ML on core expertise, not fleeting trends:

Every week, I publish no-fluff deep dives on topics that truly matter to your skills for ML/DS roles.

For instance:

• A Crash Course on Graph Neural Networks – Part 1

• A Crash Course on Graph Neural Networks – Part 2

• A Crash Course on Graph Neural Networks – Part 3

• Formulating and Implementing XGBoost From Scratch

• A Crash Course of Model Calibration – Part 1

• A Crash Course of Model Calibration – Part 2

• Conformal Predictions: Build Confidence in Your ML Model’s Predictions

• Quantization: Optimize ML Models to Run Them on Tiny Hardware

• A Crash Course on Causality – Part 1

• A Crash Course on Causality – Part 2

• 5 Must-Know Ways to Test ML Models in Production (Implementation Included)

• A Beginner-Friendly Guide to Multi-GPU Model Training

• Implementing Parallelized CUDA Programs From Scratch Using CUDA Programming

• And many many more.

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