Remove Data Collection Remove Data Processing Remove Metadata
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The Struggle Between Data Dark Ages and LLM Accuracy

Cloudera

The AI Forecast: Data and AI in the Cloud Era , sponsored by Cloudera, aims to take an objective look at the impact of AI on business, industry, and the world at large. It could be metadata that you weren’t capturing before. But what does that future look like? To get to a full 100%, that last 5% is even more valuable.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO Business Intelligence

This required dedicated infrastructure and ideally a full MLOps pipeline (for model training, deployment and monitoring) to manage data collection, training and model updates. Content management systems: Content editors can search for assets or content using descriptive language without relying on extensive tagging or metadata.

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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. You might have millions of short videos , with user ratings and limited metadata about the creators or content. If you can’t walk, you’re unlikely to run.

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Business Intelligence for Fairs, Congresses and Exhibitions

Smart Data Collective

If you occasionally run business stands in fairs, congresses and exhibitions, business stands designers can incorporate business intelligence to aid in better business and client data collection. Business intelligence tools can include data warehousing, data visualizations, dashboards, and reporting.

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Top 15 data management platforms

CIO Business Intelligence

Advertisers use OnAudience to build an understanding of their audience from data collected from multiple sources. It integrates data across a wide arrange of sources to help optimize the value of ad dollar spending. Along the way, metadata is collected, organized, and maintained to help debug and ensure data integrity.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

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Addressing the Three Scalability Challenges in Modern Data Platforms

Cloudera

Explosion of data availability from a variety of sources, including on-premises data stores used by enterprise data warehousing / data lake platforms, data on cloud object stores typically produced by heterogenous, cloud-only processing technologies, or data produced by SaaS applications that have now evolved into distinct platform ecosystems (e.g.,