Test-Driven Development for Machine Learning

Many machine learning models fail in production not because of bad mathematics, but due to fragile, untested software code. Transitioning your machine learning experiments into robust, deployable applications requires a systematic approach to software quality. In this course, you will learn how to apply Test-Driven Development (TDD) principles specifically to machine learning workflows. You will transition from writing experimental notebooks to building clean, modular, and fully tested machine learning pipelines that run reliably in production environments. What you'll learn: Learn the core principles of Test-Driven Development and how they apply to data science; Write robust unit tests for data preprocessing and feature engineering using pytest; Implement data validation checks to catch schema changes and data drift early; Mock external dependencies to ensure fast, isolated, and repeatable test execution; Set up basic automated testing workflows to validate your models continuously; Design modular machine learning code that is easy to maintain and refactor. This course begins with foundational software testing concepts and TDD terminology, then guides you step-by-step through testing data pipelines, model training functions, and deployment code. It is designed for beginner machine learning engineers, data scientists, and developers looking to improve their software engineering skills, with no prior testing experience required. Start writing dependable machine learning code today.