Exam Professional Machine Learning Engineer topic 1 question 157 discussion

Isolating the Issue: Training on a small subset helps isolate the problem to the model itself rather than the entire training pipeline or large dataset. Efficiency: Debugging with a small dataset is faster, allowing you to iterate through potential solutions quicker. Identifying Fundamental Issues: If the model struggles to learn even on a small dataset, it indicates a more fundamental problem in the model architecture, data preprocessing, or learning algorithm.

Verifying that your model can obtain a low loss on a small subset of the dataset is a good first step for debugging because it helps you determine if your model is capable of fitting the data and learning from it. If your model cannot fit a small subset of the data, it may indicate issues with the model architecture, initialization, or optimization algorithm. By starting with a small subset, you can identify and fix these issues more quickly, before moving on to larger-scale training and more complex debugging tasks.

I choose A

the first step to quickly debug the deep learning model is to verify that it can obtain a low loss on a small subset of the dataset (Option A). If the model fails to achieve good results on the smaller subset, further investigation is required to identify and address potential issues with the model.