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20 Machine Learning/Artificial Intelligence Influencers To Follow In 2020

Hacker Earth Developers Blog

Machine Learning (ML) is emerging as one of the hottest fields today. The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% billion by the end of 2025. billion by the end of 2025.

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20 Machine Learning/Artificial Intelligence Influencers To Follow In 2020

Hacker Earth Developers Blog

Machine Learning (ML) is emerging as one of the hottest fields today. The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% billion by the end of 2025. billion by the end of 2025.

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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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Rethinking ‘Big Data’ — and the rift between business and data ops

CIO

When it broke onto the IT scene, Big Data was a big deal. Still, CIOs should not be too quick to consign the technologies and techniques touted during the honeymoon period (circa 2005-2015) of the Big Data Era to the dust bin of history. Data is the cement that paves the AI value road.

Big Data 197
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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations.

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Progress for big data in Kubernetes

O'Reilly Media - Data

It has become much more feasible to run high-performance data platforms directly inside Kubernetes. The problem is that data lasts a long time and takes a long time to move. The life cycle of data is very different than the life cycle of applications. That doesn’t work out well if you have a lot of state in a few containers.

Big Data 226
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The future of data: A 5-pillar approach to modern data management

CIO

In todays economy, as the saying goes, data is the new gold a valuable asset from a financial standpoint. A similar transformation has occurred with data. More than 20 years ago, data within organizations was like scattered rocks on early Earth.

Data 167