Testing and Automating ML Pipelines

Real-world machine learning systems change over time as data shifts, making automated testing a critical requirement for production pipelines. This course teaches you how to establish structured testing to catch errors before they impact your users. You will transition from manual model evaluation to building automated testing workflows that validate data quality, code correctness, and model performance. Through clear written explanations and practical code examples, you will learn to implement robust checks at every stage of your machine learning pipeline. What you'll learn: - Understand foundational ML testing concepts, including data validation, model regression, and pipeline integrity. - Implement automated tests using pytest to verify data preprocessing and feature engineering steps. - Detect data and concept drift to ensure your model remains accurate as real-world data changes. - Evaluate model performance metrics automatically during training and deployment phases. - Configure basic CI/CD workflows to run your ML pipeline tests automatically. The course begins with core definitions and the fundamentals of machine learning system degradation, then guides you step-by-step through writing automated test suites, validating data inputs, and monitoring model outputs. This course is designed for aspiring ML engineers, data scientists, and developers who are new to testing and want to build reliable machine learning pipelines. No prior testing experience is required. Start building stable, production-ready machine learning pipelines today.