“Since AI tracks the history of all components, it can find pattern deviations much more efficiently than a human operator and point out the exact failure. In one case, for instance, we detected that adding extra CPU cores and scaling a system vertically was not as efficient as scaling it horizontally, since the system also had to deal with many TCP/IP requests. Without detecting this root cause, an IT failure would be imminent, while the management would live under the false impression that the extra CPU cores have solved the problem.”
“Microsoft said Wednesday that it is entering a strategic partnership with financial technology startup ZestFinance to make it easier for its financial services customers to adopt AI and machine learning tools.
The answer we get with deep learning might be accurate, but in the case of credit applications, that isn’t enough.”
What Zest does is to take that technique SHAP (“SHapley Additive exPlanation”) and apply it to financial underwriting so financial institutions can understand why the machine learning model said yes to one credit application and no to another. “Our version is designed to satisfy the regulatory and risk management requirements in financial services,” Merrill says.”