Remove 2021 Remove Machine Learning Remove Modeling Remove Testing
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5 things on our data and AI radar for 2021

O'Reilly on Data

Here are some of the most significant themes we see as we look toward 2021. MLOps attempts to bridge the gap between Machine Learning (ML) applications and the CI/CD pipelines that have become standard practice. The Time Is Now to Adopt Responsible Machine Learning. What will that lead to in 2021?

Data Lake 289
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TransUnion transforms its business model with IT

CIO Business Intelligence

billion acquisition of data and analytics company Neustar in 2021, TransUnion has expanded into other services such as marketing, fraud detection and prevention, and robust analytical services. The multilayered data platform will enable TransUnion’s customers to perform deep analytics and build complex AI models. But following its $3.1

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Next Stop – Predicting on Data with Cloudera Machine Learning

Cloudera

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Predictive Analytics – AI & machine learning. This integration is key in assuring that models evolve with the data – to avoid, for example, model drift.

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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.

Risk 111
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Five Trends for the Financial Services Industry to Track in 2021

Cloudera

2021 is going to be the year when the financial services industry reckons with how these changes will play out, impacting business operations, processes, new technologies, and, of course, new regulations. Artificial intelligence and machine learning (AI/ML) will be central to risk modeling in 2021 and the future.

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How to Launch Your AI Projects from Pilot to Production – and Ensure Success

CIO Business Intelligence

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects. CIOs and CDOs should lead ModelOps and oversee the lifecycle Leaders can review and address issues if the data science teams struggle to develop models.

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AI Powered Misinformation and Manipulation at Scale #GPT-3

O'Reilly on Data

GPT-3 is essentially an auto-complete bot whose underlying Machine Learning (ML) model has been trained on vast quantities of text available on the Internet. Three ideas to set the stage: OpenAI is not the only organization to have powerful language models. These models will continue to become more powerful.

Modeling 346