Data Integration Predictive Modeling Risk Management Workshop 
O'Reilly on Data
DECEMBER 12, 2019
That’s where model debugging comes in. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Sensitivity analysis.
Cloudera
SEPTEMBER 7, 2023
Perhaps the biggest challenge of all is that AI solutions—with their complex, opaque models, and their appetite for large, diverse, high-quality datasets—tend to complicate the oversight, management, and assurance processes integral to data management and governance. AI-ify risk management.
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