Machine learning systems shift over time, making structured testing essential. In this short course, you’ll learn how to evaluate ML pipelines using unit, integration, and smoke tests and how to detect data drift across critical features. You will also create automated regression test suites that compare new model outputs to golden datasets, helping you catch degradation early and deploy reliably. Through concise videos, readings, hands-on practice, and guided coaching, you’ll define meaningful ML test cases and configure nightly pytest suites. By the end, you will have a practical, reusable testing framework you can apply directly to real-world ML pipelines.