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What is Model Risk and Why Does it Matter?

DataRobot Blog

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. When business decisions are made based on bad models, the consequences can be severe. As machine learning advances globally, we can only expect the focus on model risk to continue to increase.

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Accomplish Agile Business Intelligence & Analytics For Your Business

datapine

Your Chance: Want to test an agile business intelligence solution? It’s necessary to say that these processes are recurrent and require continuous evolution of reports, online data visualization , dashboards, and new functionalities to adapt current processes and develop new ones. Collaboratively develop reports.

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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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Cathay Pacific to take cloud journey to new heights

CIO Business Intelligence

But in this next cloud optimization phase, the airliner will focus on enhancing the security and performance of these workloads on the cloud, says Nair, who was hired by Cathay in 2011 as an application service manager, working his way up to his current position 10 years later.

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Smarter Survey Results and Impact: Abandon the Asker-Puker Model!

Occam's Razor

If you are open to being challenged… then here are the short-stories inside this post… The World Needs Reporting Squirrels. Bonus #2: The Askers-Pukers Business Model. The World Needs Reporting Squirrels. If you are curious, here is a April 2011 post: The Difference Between Web Reporting And Web Analysis.

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Becoming a machine learning company means investing in foundational technologies

O'Reilly on Data

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.

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How to Optimize Marketing and Sales Operations

Jedox

They specialize in technology infrastructure, data and analytics of the go-to-market processes to measure the effectiveness of each channel, the overall performance of the marketing program and the sales organization, and support customer, product and market analysis and A/B testing. Collaboration and integration are key.

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