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 and maintaining machine learning pipelines, and the quality of training and inference data directly affects model behavior. You want a clear approach for catching bad data early, keeping runs reproducible, and making pipeline failures visible.
How do you ensure data quality in your machine learning projects?