Machine learning (ML) model debugging is essential for understanding and fixing issues related to accuracy, fairness, and security in ML systems, as traditional software debugging tools are insufficient due to the complexity of ML models, which involve dynamic code, datasets, and model weights. Effective debugging requires a multi-stage strategy focusing on data quality, model building, and output testing, with tools like Neptune.ai, Weights and Biases, Comet, and various open-source libraries such as Cerberus, Deequ, and Great Expectations facilitating real-time monitoring, data validation, and model interpretation. Model interpretability tools like Alibi, Captum, and Shap, as well as visual debugging tools such as Manifold and TensorWatch, help in understanding model decisions and performance issues. Prediction-centric debugging using these tools allows for a deeper insight into model failures, ultimately aiding in the development of robust and reliable ML systems.