Absolutely! In my experience, the biggest challenge is often dealing with hidden biases and inconsistencies in the data.

For example, models trained on historical data can unintentionally learn patterns that reflect past errors or systemic bias.

One approach that worked well for me was rigorous data validation and augmentation checking for missing values, outliers, and distribution mismatches, and creating synthetic data where appropriate.

Another key lesson is to iterate quickly with smaller prototypes before scaling up, so you can catch issues early without investing too much in a flawed model.