Remove Big Data Remove Examples Remove Machine Learning
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Lessons learned turning machine learning models into real products and services

O'Reilly Media - Data

Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. However, in talking with CEOs looking to implement machine learning in their organizations, there seems to be a common problem in moving machine learning from science to production.

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The Future of Machine Learning in Cybersecurity

CIO

Machine learning (ML) is a commonly used term across nearly every sector of IT today. And while ML has frequently been used to make sense of big data—to improve business performance and processes and help make predictions—it has also proven priceless in other applications, including cybersecurity.

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Progress for big data in Kubernetes

O'Reilly Media - Data

An example of how pods interact to provide access to a shared data platform in a Kubernetes system. One node also runs a shared client-access process that is used by an application pod to access data in the data platform formed by the low-level storage services. The implications for big data. Future outlook.

Big Data 231
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Quantexa raises $153M to build out AI-based big data tools to track risk and run investigations

TechCrunch

to bring big data intelligence to risk analysis and investigations. Quantexa’s machine learning system approaches that challenge as a classic big data problem — too much data for a human to parse on their own, but small work for AI algorithms processing huge amounts of that data for specific ends. .

Big Data 257
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As extreme weather events worsen, 7Analytics meshes AI and big data to predict flooding

TechCrunch

One of these companies is 7Analytics , a Norwegian startup founded back in 2020 by a team of data scientists and geologists to reduce the risks of flooding for construction and energy infrastructure companies. ” Startups to the rescue?

Big Data 233
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Data engineers vs. data scientists

O'Reilly Media - Data

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.

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List of Top 10 Machine Learning Examples in Real Life

Openxcell

But with technological progress, machines also evolved their competency to learn from experiences. This buzz about Artificial Intelligence and Machine Learning must have amused an average person. But knowingly or unknowingly, directly or indirectly, we are using Machine Learning in our real lives.