Ensuring the robustness and reliability of machine learning (ML) models is critical for production-grade applications. Unlike traditional software systems, ML models are stochastic, data-dependent, and evolve. This makes testing and debugging more complicated. Errors in ML systems can arise from different sources, such as model training bugs, data pipeline issues, hyperparameter misconfigurations, and model drift. By implementing a systematic method for testing and debugging ML models, you ensure reproducibility, consistency, and fairness in your project. In addition, you decrease failures in practical deployments. This chapter focuses on best practices for testing and debugging ML systems, such as