Debugging in the realm of machine learning (ML) encompasses the crucial process of detecting and rectifying errors within an ML model. These errors, commonly referred to as bugs, have the potential to arise at multiple junctures throughout the ML pipeline. Such stages range from the initial phases of data preparation and feature engineering to the subsequent steps of model training and deployment.
