Data Processing Metrics Risk Risk Management 
CIO Business Intelligence
MARCH 18, 2024
There are a lot of risks and a lot of land mines to navigate,” says the analyst. Coming to grips with risk The first step in making any bet — or investment — is to understand your ability to withstand risk. Our data team uses gen AI on Amazon cloud to explore sustainability metrics. The opportunity is too big.
Cloudera
OCTOBER 20, 2021
As part of these efforts, disclosure requirements will mandate that firms provide “the impact of a company’s activities on the environment and society, as well as the business and financial risks faced by a company due to its sustainability exposures.” What are the key climate risk measurements and impacts? They need to understand;
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DataRobot Blog
JUNE 27, 2022
If the assumptions are being breached due to fundamental changes in the process being modeled, the deployed system is not likely to serve its intended purpose, thereby creating further model risk that the institution must manage. Monitoring Model Metrics. Figure 1: Data drift tab of a deployed DataRobot model.
DataRobot Blog
MAY 26, 2022
To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.
CIO Business Intelligence
NOVEMBER 21, 2023
Ask IT leaders about their challenges with shadow IT, and most will cite the kinds of security, operational, and integration risks that give shadow IT its bad rep. That’s not to downplay the inherent risks of shadow IT.
IBM Big Data Hub
FEBRUARY 8, 2024
It refers to a set of metrics used to measure an organization’s environmental and social impact and has become increasingly important in investment decision-making over the years. In response, asset managers began to develop ESG strategies and metrics to measure the environmental and social impact of their investments.
O'Reilly on Data
DECEMBER 12, 2019
1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. That’s where model debugging comes in. Sensitivity analysis.
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