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Automating Model Risk Compliance: Model Monitoring

DataRobot Blog

In our previous two posts, we discussed extensively how modelers are able to both develop and validate machine learning models while following the guidelines outlined by the Federal Reserve Board (FRB) in SR 11-7. Monitoring Model Metrics.

Risk 59
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How to Gain Greater Confidence in your Climate Risk Models

Cloudera

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? Generate Scenarios.

Risk 79
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Automating Model Risk Compliance: Model Validation

DataRobot Blog

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52
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12 Cloud Computing Risks & Challenges Businesses Are Facing In These Days

datapine

Instead of installing software on your own servers, SaaS companies enable you to rent software that’s hosted, this is typically the case for a monthly or yearly subscription fee. More and more CRM, marketing, and finance-related tools use SaaS business intelligence and technology, and even Adobe’s Creative Suite has adopted the model.

Risk 237
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The risks and limitations of AI in insurance

IBM Big Data Hub

This blog continues the discussion, now investigating the risks of adopting AI and proposes measures for a safe and judicious response to adopting AI. Risk and limitations of AI The risk associated with the adoption of AI in insurance can be separated broadly into two categories—technological and usage.

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Private cloud makes its comeback, thanks to AI

CIO Business Intelligence

Private cloud providers may be among the key beneficiaries of today’s generative AI gold rush as, once seemingly passé in favor of public cloud, CIOs are giving private clouds — either on-premises or hosted by a partner — a second look. You don’t want a mistake to happen and have it end up ingested or part of someone else’s model.

IT 139
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Why you should care about debugging machine learning models

O'Reilly on Data

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]