Debugging machine learning models often involves more than just correcting a single line of code. Engineers must analyze the model's behavior and understand the underlying data.
The process of fixing bugs can require slicing the data, retraining the model, and validating the results to ensure that the changes have a positive impact.
Evaluating the success of these fixes is crucial, as it determines whether the model performs better and meets the desired outcomes.