
Shreya Shankar — Operationalizing Machine Learning
Gradient Dissent: Conversations on AI
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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