Machine Learning in Production: Why You Should Care About Data and Concept Drift

Machine learning models often deal with corrupted, late, or incomplete data. Data quality issues account for a major share of failures in production. But let's say it is covered. The data engineering team does a great job, data owners and producers do no harm, and no system breaks. Does this mean our model is safe? Sadly, this is never a given.