Data Quality, Predictive Modeling, Risk Management and Workshop

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

Cloudera

SEPTEMBER 7, 2023

The way to manage this is by embedding data integration, data quality-monitoring, and other capabilities into the data platform itself , allowing financial firms to streamline these processes, and freeing them to focus on operationalizing AI solutions while promoting access to data, maintaining data quality, and ensuring compliance.

Insurance

Insurance Risk Data-driven Finance

Why you should care about debugging machine learning models

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.

Machine Learning

Machine Learning Modeling Testing Risk Management

Data Leaders Brief

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

Why you should care about debugging machine learning models

Webinars

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