Remove Contextual Data Remove Data Lake Remove IT
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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Why: Data Makes It Different. In contrast, a defining feature of ML-powered applications is that they are directly exposed to a large amount of messy, real-world data which is too complex to be understood and modeled by hand. However, the concept is quite abstract. Can’t we just fold it into existing DevOps best practices?

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Regeneron turns to IT to accelerate drug discovery

CIO Business Intelligence

In that capacity, he knew that, in addition to having the right team and technical building blocks in place, data was the key to Regeneron’s future success. “It It is all about the data. Everything we do is data-driven, and at that time, we were very datacenter-driven but the technology had lots of limitations” says McCowan. “It

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4 ways generative AI addresses manufacturing challenges

IBM Big Data Hub

Or we create a data lake, which quickly degenerates to a data swamp. Contextual data understanding Data systems often cause major problems in manufacturing firms. IBM built a workforce advisor that uses summarization and contextual data understanding with intent detection and multi-modal interaction.

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Addressing the Elephant in the Room – Welcome to Today’s Cloudera

Cloudera

There were thousands of attendees at the event – lining up for book signings and meetings with recruiters to fill the endless job openings for developers experienced with MapReduce and managing Big Data. This was the gold rush of the 21st century, except the gold was data. That is the key to our open data lakehouse architecture.

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The Award Winning Formula: How Cloudera Empowered OCBC With Trusted Data To Unlock Business Value from AI

Cloudera

To keep pace as banking becomes increasingly digitized in Southeast Asia, OCBC was looking to utilize AI/ML to make more data-driven decisions to improve customer experience and mitigate risks. While these are great proof points to demonstrate how business value can be driven by AI/ML, this was only made possible with trusted data.

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Achieving Trusted AI in Manufacturing

Cloudera

Here are some of the key use cases: Predictive maintenance: With time series data (sensor data) coming from the equipment, historical maintenance logs, and other contextual data, you can predict how the equipment will behave and when the equipment or a component will fail. Eliminate data silos.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.