Remove Measurement Remove Risk Management Remove Software Remove Testing
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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. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

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3 areas where gen AI improves productivity — until its limits are exceeded

CIO Business Intelligence

We did side-by-side testing,” he says. So based on early results, the three functional areas where companies see the biggest productivity improvements are in customer service, software development, and general creative and knowledge work. But it’s not just big software development projects where gen AI can help.

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10 hottest IT jobs for salary growth in 2023

CIO Business Intelligence

Skills such as software engineering, architecture, cloud, and program management are highly sought after as more companies explore creating both internal and external applications and solutions. Relevant skills for DevOps Engineers include automation, software development, system administration skills, and cloud computing.

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AI Technology is Invaluable for Cybersecurity

Smart Data Collective

As a result, businesses across many industries have been spending increasingly large sums on security technology and services, driving demand for trained specialists fluent in the latest preventative measures. After evaluating potential risks, cybersecurity professionals implement various preventative actions.

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Data protection strategy: Key components and best practices

IBM Big Data Hub

A data protection strategy is a set of measures and processes to safeguard an organization’s sensitive information from data loss and corruption. Data risk management To protect their data, organizations first need to know their risks. What is a data protection strategy?

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Why you should care about debugging machine learning models

O'Reilly on Data

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. While our analysis of each method may appear technical, we believe that understanding the tools available, and how to use them, is critical for all risk management teams.

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What to Do When AI Fails

O'Reilly on Data

Before we get into the details of AI incident response, it’s worth raising these baseline questions: What makes AI different from traditional software systems? The answers boil down to three major reasons, which may also exist in other large software systems but are exacerbated in AI. All predictive models are wrong at times?—just

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