Remove Predictive Modeling Remove Risk Management Remove Workshop
article thumbnail

Why you should care about debugging machine learning models

O'Reilly on Data

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.

article thumbnail

CDOs: Your AI is smart, but your ESG is dumb. Here’s how to fix it

CIO Business Intelligence

Highlight how ESG metrics can enhance risk management, regulatory compliance and brand reputation. Predictive modeling can help companies optimize energy consumption, while AI-driven insights can identify supply chain inefficiencies that lead to excessive waste.

IT 59
article thumbnail

How Financial Services and Insurance Streamline AI Initiatives with a Hybrid Data Platform

Cloudera

Develop workshops, e-learning modules, and hands-on sessions designed to familiarize employees with the fundamentals of AI and its applications within the finance sector. AI-ify risk management. Practice real-time risk management. Automate wealth management. Train and upskill employees.