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Shreya Shankar — Operationalizing Machine Learning

Gradient Dissent: Conversations on AI

CHAPTER

Monitoring for Data Corruption Is More Important Than Monitoring for Data Drift

The probability that at least one record and one column is corrupted is so high. You've got models of production with tens of thousands of features. The problem is like, again, when does it get so bad that it brings down the business? And how do I find that pretty precisely?

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