ML Model Testing and Debugging for Production Systems

Deploying machine learning models to production requires more than just training a model; you must ensure it behaves predictably under real-world conditions. This text-based course guides you through the essential methodologies for identifying, diagnosing, and fixing errors in your machine learning workflows. You will transition from writing fragile experimental scripts to building resilient, testable machine learning systems. Through clear written explanations, code examples, and conceptual exercises, you will learn how to validate your data, test your model's performance, and monitor its behavior over time. What you'll learn: - Understand foundational concepts of machine learning system reliability and common failure modes. - Apply unit testing principles to data preprocessing pipelines and feature engineering code. - Validate model performance using behavioral testing, bias checks, and boundary cases. - Configure simple data validation checks to prevent dirty data from corrupting your training pipeline. - Monitor deployed models for data drift and concept drift to maintain accuracy over time. - Practice debugging techniques to systematically isolate and resolve training and inference errors. The course begins with core definitions and structural concepts before moving into practical code-level testing strategies and modern monitoring workflows. You will read through realistic scenarios and practice implementing robust validation checks using standard pythonic patterns. This course is designed for aspiring ML engineers, data scientists, and software developers who understand basic Python and want to build reliable ML systems. No advanced DevOps or production experience is required. Start reading today to build machine learning systems that you can confidently deploy and maintain.