From my experience, the toughest part of deep learning projects isn’t always the model itself, but everything around it. Cleaning and labeling large datasets can take far more time than training, and even small inconsistencies in data can derail performance. When it comes to architectures and hyperparameters, I found that incremental experimentation starting simple, benchmarking, and then layering in complexity works better than chasing the “perfect” setup from the start. Interpreting results is another hurdle; visualization tools and explainability methods like SHAP or LIME have been really helpful in bridging the gap between raw outputs and insights that stakeholders can actually trust. What’s worked best for me is keeping the loop tight: validate early, monitor closely, and never assume the model’s success in theory will directly translate to production.