Lilly Tech Systems
Unit Testing for ML Pipelines
Learn to write robust unit tests for machine learning code using pytest, covering data transformations, feature engineering, model training functions, and CI integration.
Course Lessons
Work through these lessons sequentially or jump to the topic most relevant to you.
1. Why Unit Test ML Code
The case for unit testing in machine learning projects
2. Setting Up pytest for ML
Configuring pytest for machine learning projects
3. Testing Data Transformations
Writing tests for data preprocessing and transformation logic
4. Testing Feature Engineering
Verifying feature engineering logic with automated tests
5. Testing Model Training Functions
Testing the training loop and model behavior
6. Mocking External Services
Isolating ML tests from external dependencies
7. CI Integration for ML Tests
Running ML tests in continuous integration pipelines
What You'll Learn
By the end of this course, you will be able to:
Core Concepts
Understand the fundamental principles and techniques of unit testing for ml pipelines for production AI systems.
Practical Skills
Build hands-on skills with real code examples, frameworks, and tools used by industry professionals.
Best Practices
Apply industry best practices and avoid common pitfalls when implementing testing in your ML projects.
Production Ready
Ship reliable, well-tested AI systems with confidence using automated testing pipelines.