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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

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AI poised to replace entry-level positions at large financial institutions

CIO Business Intelligence

Large banking firms are quietly testing AI tools under code names such as as Socrates that could one day make the need to hire thousands of college graduates at these firms obsolete, according to the report.

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How Svevia connects roads, risk, and refuse through the cloud

CIO Business Intelligence

With the right data available and Microsoft’s Power platform, the aim is to proactively issue reports and decision support on an ongoing basis, and provide the power to digitize all parts of the company. Digital alerts Another project deals with slow-moving vehicles, something that increases the risk of accidents on the roads.

Risk 76
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The new CIO mandate: Selling AI to employees

CIO Business Intelligence

As organizations roll out AI applications and AI-enabled smartphones and devices, IT leaders may need to sell the benefits to employees or risk those investments falling short of business expectations. They need to have a culture of experimentation.” CIOs should be “change agents” who “embrace the art of the possible,” he says.

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Bringing an AI Product to Market

O'Reilly on Data

Many companies face a problem that’s even worse: no one knows which levers contribute to the metrics that impact business outcomes, or which metrics are important to the company (such as those reported to Wall Street by publicly-traded companies). Without clarity in metrics, it’s impossible to do meaningful experimentation.

Marketing 361
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3 key digital transformation priorities for 2024

CIO Business Intelligence

The analyst reports tell CIOs that generative AI should occupy the top slot on their digital transformation priorities in the coming year. Moreover, the CEOs and boards that CIOs report to don’t want to be left behind by generative AI, and many employees want to experiment with the latest generative AI capabilities in their workflows.

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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. What Is Model Risk?