[P] ML project (XGBoost + Databricks + MLflow) — how to talk about “production issues” in interviews?

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Hey all, I recently built an end-to-end fraud detection project using a large banking dataset: Trained an XGBoost model Used Databricks for processing Tracked experiments and deployment with MLflow The pipeline worked well end-to-end, but I’m realizing something during interview prep: A lot of ML Engineer interviews (even for new grads) expect discussion around: What can go wrong in production How you debug issues How systems behave at scale To be honest, my project ran pretty smoothly, so I ...