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What are model governance and model operations?

O'Reilly on Data

A catalog or a database that lists models, including when they were tested, trained, and deployed. Model operations, testing, and monitoring. As machine learning proliferates in products and services, we need a set of roles, best practices, and tools to deploy, manage, test, and monitor ML in real-world production settings.

Modeling 249
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What is Model Risk and Why Does it Matter?

DataRobot Blog

This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses. The stakes in managing model risk are at an all-time high, but luckily automated machine learning provides an effective way to reduce these risks.

Risk 111
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What Are ChatGPT and Its Friends?

O'Reilly on Data

What is it, how does it work, what can it do, and what are the risks of using it? All of these models are based on a technology called Transformers , which was invented by Google Research and Google Brain in 2017. It’s by far the most convincing example of a conversation with a machine; it has certainly passed the Turing test.

IT 346
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What Are the Most Important Steps to Protect Your Organization’s Data?

Smart Data Collective

After a marginal increase in 2015, another steep rise happened in 2016 through 2017 before the volume decreased in 2018 and rose in 2019, and dropped again in 2020. Similarly, in 2018 the volume of breaches dropped to 1.257 billion (from 1.632 billion in 2017), but the records exposed dramatically increased to 471.23 million in 2017).

Testing 131
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Streaming Market Data with Flink SQL Part II: Intraday Value-at-Risk

Cloudera

These interactions are captured and the resulting synthetic data sets can be analysed for a number of applications, such as training models to detect emergent fraudulent behavior, or exploring “what-if” scenarios for risk management. Value-at-Risk (VaR) is a widely used metric in risk management. Intraday VaR. Citations. [1]

Risk 99
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Agile Technology and Big Data Improve the State of Cybersecurity

Smart Data Collective

The risk of data breaches is rising sharply. The number increased 56% between 2017 and 2018. Cybersecurity experts are using data analytics and AI to identify warning signs that a firewall has been penetrated, conduct risk scoring analyses and perform automated cybersecurity measures.

Big Data 126
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7 famous analytics and AI disasters

CIO Business Intelligence

In 2017, The Economist declared that data, rather than oil, had become the world’s most valuable resource. MIT Technology Review has chronicled a number of failures, most of which stem from errors in the way the tools were trained or tested. The algorithm learned to identify children, not high-risk patients.

Analytics 145