How do you test and debug AI code?

1 Define your objectives

Before you start writing or testing your AI code, you need to have a clear idea of what you want to achieve and how you will measure it. This means defining your problem statement, your success criteria, your assumptions, and your limitations. You also need to identify your target users, their needs, and their expectations. Having a well-defined objective will help you design your AI code, select your data sources, choose your algorithms, and evaluate your results.

2 Use version control

Version control is a system that tracks the changes you make to your code and allows you to revert to previous versions if needed. Version control is crucial for AI code, as it enables you to document your experiments, compare different versions, collaborate with other developers, and reproduce your results. You can use version control tools, such as Git, to manage your AI code and data. You can also use platforms, such as GitHub or GitLab, to host your code and data online and share them with others.

3 Test your data

Data is the fuel of AI code, so you need to make sure that your data is clean, consistent, and relevant. Testing your data involves checking for errors, outliers, missing values, duplicates, and inconsistencies. You also need to verify that your data matches your objectives, your assumptions, and your users' expectations. You can use tools, such as pandas or PySpark, to explore and manipulate your data. You can also use tools, such as TensorFlow Data Validation or Great Expectations, to validate and monitor your data quality.

4 Test your models

Models are the core of AI code, so you need to make sure that your models are accurate, robust, and fair. Testing your models involves evaluating their performance, their generalization, and their bias. You also need to verify that your models meet your objectives, your criteria, and your users' needs. You can use tools, such as scikit-learn or PyTorch, to train and test your models. You can also use tools, such as TensorFlow Model Analysis or Fairlearn, to analyze and improve your models' fairness.

5 Test your algorithms

Algorithms are the logic of AI code, so you need to make sure that your algorithms are correct, efficient, and secure. Testing your algorithms involves verifying their functionality, their complexity, and their vulnerability. You also need to verify that your algorithms match your objectives, your assumptions, and your users' expectations. You can use tools, such as pytest or unittest, to write and run unit tests for your algorithms. You can also use tools, such as Codecov or SonarQube, to measure and improve your code coverage and quality.

6 Debug your code

Debugging is the process of finding and fixing errors in your code. Debugging AI code can be tricky, as the errors may not be obvious or easy to locate. Moreover, debugging AI code may require you to understand the behavior and the output of your data, models, and algorithms. You can use tools, such as pdb or IPython, to inspect and modify your code interactively. You can also use tools, such as TensorFlow Debugger or PyTorch Lightning, to debug your code at different levels of abstraction.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?