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What happens when you mess up data modeling?

Xebia

There has been an uptick in discussions surrounding the modern data stack, data mesh and self-service promising to solve many of your company’s data problems. Knowing a data modeling technique versus implementing it are two entirely different things.

Data 130
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Moving Enterprise Data From Anywhere to Any System Made Easy

CIO

Since 2015, the Cloudera DataFlow team has been helping the largest enterprise organizations in the world adopt Apache NiFi as their enterprise standard data movement tool. This need has generated a market opportunity for a universal data distribution service. Why does every organization need it when using a modern data stack?

System 162
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Machine learning model serving architectures

Xebia

After months of crunching data, plotting distributions, and testing out various machine learning algorithms you have finally proven to your stakeholders that your model can deliver business value. In this blog we will discuss the most common serving architectures 1 ; batch predicting, on-demand synchronous serving and streaming serving.

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The year’s top 10 enterprise AI trends — so far

CIO

AI is now a board-level priority Last year, AI consisted of point solutions and niche applications that used ML to predict behaviors, find patterns, and spot anomalies in carefully curated data sets. With a pre-trained model, you can bring it into HR, finance, IT, customer service—all of us are touched by it.”

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Accelerate 5G with AI-Powered Cybersecurity

Palo Alto Networks

For example, workers equipped with AI-enhanced wearables connected to a private 5G network could detect hazardous gasses in real time and leverage connected data to enhance their well-being. This is because network architecture gets increasingly complex (and effective) the more it connects with devices, networks and services (i.e.,

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Setting up and Getting Started with Cloudera’s New SQL AI Assistant

Cloudera

As described in our recent blog post , an SQL AI Assistant has been integrated into Hue with the capability to leverage the power of large language models (LLMs) for a number of SQL tasks. This is a real game-changer for data analysts on all levels and will make SQL development faster, easier, and less error-prone.

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We are still early with the cloud

Erik Bernhardsson

This is is in many respects a successor to a blog post I wrote last year. about what I want from software infrastructure, but the ideas morphed in my head into something sort of wider. I encountered AWS in 2006 or 2007 and remember thinking that it's crazy — why would anyone want to put their stuff in someone else's data center?

Cloud 254