What are some common tools used for debugging machine learning models in production?

Common tools for debugging machine learning models in production include TensorFlow Debugger (TF Debugger), PyTorch Lightning for structured model development, and Weights & Biases for tracking experiments and visualizing metrics. Additionally, libraries like ELI5 and SHAP can help understand model predictions and interpret results, while tools such as Great Expectations provide data validation to catch anomalies in input data. These tools enhance the troubleshooting process by providing insights into model behavior and data integrity.