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Data governance in the age of generative AI

AWS Big Data

Data governance is a critical building block across all these approaches, and we see two emerging areas of focus. First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

A RAG-based generative AI application can only produce generic responses based on its training data and the relevant documents in the knowledge base. Streaming jobs constantly ingest new data to synchronize across systems and can perform enrichment, transformations, joins, and aggregations across windows of time more efficiently.

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Unstructured data management and governance using AWS AI/ML and analytics services

AWS Big Data

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Unstructured information may have a little or a lot of structure but in ways that are unexpected or inconsistent. Text, images, audio, and videos are common examples of unstructured data.

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A Guide to Data Analytics in the Travel Industry

Alation

To fully realize data’s value, organizations in the travel industry need to dismantle data silos so that they can securely and efficiently leverage analytics across their organizations. What is big data in the travel and tourism industry? Why is data analytics important for travel organizations?

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A Few Proven Suggestions for Handling Large Data Sets

Smart Data Collective

Data mining and knowledge go hand in hand, providing insightful information to create applications that can make predictions, identify patterns, and, last but not least, facilitate decision-making. Working with massive structured and unstructured data sets can turn out to be complicated. A document is susceptible to change.

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Preprocess and fine-tune LLMs quickly and cost-effectively using Amazon EMR Serverless and Amazon SageMaker

AWS Big Data

The Common Crawl corpus contains petabytes of data, regularly collected since 2008, and contains raw webpage data, metadata extracts, and text extracts. In addition to determining which dataset should be used, cleansing and processing the data to the fine-tuning’s specific need is required. It is continuously updated.

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The Superpowers of Ontotext’s Relation and Event Detector

Ontotext

Quality assurance process, covering gold standard creation , extraction quality monitoring, measurement, and reporting via Ontotext Metadata Studio. This semantic model serves as a blueprint or framework against which raw data is analyzed and organized. Let’s have a quick look under the bonnet.