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 data pipelines that feed model training and batch scoring. Before focusing on model changes, you want a clear approach for keeping the underlying data trustworthy as it moves through ingestion, transformation, and feature creation.
How do you ensure the quality of data used in your models?