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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. However, the concept is quite abstract.

IT 364
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Oracle launches a new sustainability app for Fusion Cloud EPM

CIO Business Intelligence

Fusion Data Intelligence — which can be viewed as an updated avatar of Fusion Analytics Warehouse — combines enterprise data, ready-to-use analytics along with prebuilt AI and machine learning models to deliver business intelligence.

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Regeneron turns to IT to accelerate drug discovery

CIO Business Intelligence

Rigid requirements to ensure the accuracy of data and veracity of scientific formulas as well as machine learning algorithms and data tools are common in modern laboratories. When Bob McCowan was promoted to CIO at Regeneron Pharmaceuticals in 2018, he had previously run the data center infrastructure for the $81.5

Data Lake 124
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Addressing the Elephant in the Room – Welcome to Today’s Cloudera

Cloudera

As a result, developers – regardless of their expertise in machine learning – will be able to develop and optimize business-ready large language models (LLMs).

Big Data 103
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Cropin’s agriculture industry cloud to provide apps, data frameworks

CIO Business Intelligence

Dubbed Cropin Cloud, the suite comes with the ability to ingest and process data, run machine learning models for quick analysis and decision making, and several applications specific to the industry’s needs.

B2B 105
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Fueling Enterprise Generative AI with Data: The Cornerstone of Differentiation

Cloudera

By feeding customer interaction data and financial transaction records into LLMs, OCBC Bank has developed AI-powered chatbots that provide personalized financial advice and support. The bank has also used GenAI to automate document processing, reducing manual effort and improving efficiency.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.