In my experience, deploying ML models in the real world is always more challenging than it looks on paper. I’ve often encountered messy or incomplete data, and even small labeling errors sometimes caused models to behave unpredictably.

To tackle this, I spent time on careful data cleaning, feature engineering, and iterative validation. I also learned the importance of understanding the business context sometimes the “obvious” features weren’t capturing the real patterns.