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What Is Model Risk Management and How is it Supported by Enterprise MLOps?

Domino Data Lab

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. What Is Model Risk?

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The Foundations of a Modern Data-Driven Organisation: Change from Within (part 2 of 2)

Cloudera

In my previous blog post, I shared examples of how data provides the foundation for a modern organization to understand and exceed customers’ expectations. Collecting workforce data as a tool for talent management. Collecting workforce data as a tool for talent management. Data enables Innovation & Agility.

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Five Ways AI Can Help States Solve Their Hardest Problems (Part 2): Mitigate Risk and Fraud

DataRobot

Procurement misuse, abuse, and inefficiency continues to be a challenge for state governments, driven by large transaction volumes, pressure to reduce costs, and staffing challenges. This can be accomplished by providing stronger accountability, increased productivity, and transparency into spending and risk management.

Risk 98
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Using Strategic Data Governance to Manage GDPR/CCPA Complexity

erwin

In light of recent, high-profile data breaches, it’s past-time we re-examined strategic data governance and its role in managing regulatory requirements. for alleged violations of the European Union’s General Data Protection Regulation (GDPR). Complexity. Five Steps to GDPR/CCPA Compliance. Govern PII “at rest”.

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The Future of RegTech for AI Governance

IBM Big Data Hub

The adoption of AI is driven by its utility and the improvements in efficiency it creates. Beyond these common uses of AI, there are also uses that regulators are beginning to identify as areas where there may be a higher risk. The use of artificial intelligence (AI) is now commonplace throughout society. References. [1] 1] [link]. [2]

Risk 98
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Simulation for better decision making

Cloudera

Over the past decade, we have observed open source powered big data and analytics platforms evolve from large data storage containers to massively scalable advanced modeling platforms that seamlessly operate on-premises and in a multi-cloud environment. These examples are well covered by many others (e.g.,

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Data-driven competitive advantage in the financial services industry

Cloudera

The same study also stated that having stronger online data security, being able to conduct more banking transactions online and having more real-time problem resolution were the top priorities of consumers. . Financial institutions need a data management platform that can keep pace with their digital transformation efforts.