For me, the toughest hurdle has always been data quality. I have worked on projects where mislabeled or unbalanced datasets completely threw off results, no matter how carefully the model was tuned. What really helped was setting up strong validation steps early catching data issues before they made their way into training. Over time, realized that 70% of the effort is cleaning and preparing the data, and only 30% is actual modeling.