Your question is Data Quality in ML Pipelines. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You're building machine learning pipelines and want to catch bad data before it affects training, features, or model outputs. You need a practical way to think about validation, recovery, and ongoing checks across the pipeline.
How do you ensure data quality in your machine learning projects?