Automate and Evaluate ML Pipeline Tests

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

• Evaluate ML pipelines with unit, integration, and smoke tests
• Detect data drift across key features
• Create automated regression test suites comparing against golden datasets
• Configure nightly pytest suites to maintain quality

• Basic familiarity with relevant concepts and terminology
• Readiness to practice through applied exercises or case studies