Found that the biggest hurdles usually come long before the model training stage. Preparing clean, reliable datasets often takes far more effort than people expect, and even small inconsistencies can throw performance off. For architectures and hyperparameters, I’ve learned that starting simple and experimenting incrementally tends to work better than chasing the “perfect” setup right away. And when it comes to interpretability, tools like SHAP or LIME have been really useful in making results more transparent for non-technical stakeholders. The hardest part is bridging the gap between a strong model in theory and one that consistently delivers in production, which often means putting equal focus on monitoring, validation, and iteration as on the model itself.