Remove category data-engineering
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Nvidia AI Enterprise adds generative AI microservices

CIO Business Intelligence

They’re split into two main categories — Nvidia NIM, which covers microservices related to deploying production AI models, and CUDA-X, for microservices like cuOpt, the company’s optimization engine. Business data hosted on Box, Cloudera, Cohesity, Datastax and the like can be used in AI applications as of version 5.0,

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Prompting Isn’t The Most Important Skill

O'Reilly on Data

Anant Agarwal, an MIT professor and of the founders of the EdX educational platform, recently created a stir by saying that prompt engineering was the most important skill you could learn. But before discussing why, it’s important to think about what prompt engineering means. And that you could learn the basics in two hours.

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Simplify your query management with search templates in Amazon OpenSearch Service

AWS Big Data

This fully managed service allows organizations to secure data, perform keyword and semantic search, analyze logs, alert on anomalies, explore interactive log analytics, implement real-time application monitoring, and gain a more profound understanding of their information landscape. He loves to travel and spend quality time with his family.

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Accelerating scope 3 emissions accounting: LLMs to the rescue

IBM Big Data Hub

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Why are Scope 3 emissions difficult to calculate?

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5 SQL Visualization Tools for Data Engineers

KDnuggets

This article will discuss SQL visualization, its role in augmenting the modern-day data engineer, and five categories of SQL visualization tools.

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The Syntax, Semantics, and Pragmatics Gap in Data Quality Validation Testing 

DataKitchen

The Syntax, Semantics, and Pragmatics Gap in Data Quality Validate Testing Data Teams often have too many things on their ‘to-do’ list. They have a backlog full of new customer features or data requests, and they go to work every day knowing that they won’t and can’t meet customer expectations.

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The InnoGraph Artificial Intelligence Taxonomy

Ontotext

This post describes our approach to developing such a taxonomy by integrating and coreferencing data from numerous sources. Here we need to filter out non-AI topics ( search-engine compression ), and we find some more recent topics ( llm llama ) as well as very specialized topics that pertain only to this library (e.g.,