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Similarity and Dissimilarity Measures in Data Science

Analytics Vidhya

For that, we need to compare, sort, and cluster various data points within the unstructured data. Similarity and dissimilarity measures are crucial in data science, to compare and quantify how similar the data points are.

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Measuring Text Similarity Using BERT

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon BERT is too kind — so this article will be touching. The post Measuring Text Similarity Using BERT appeared first on Analytics Vidhya.

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Unbundling the Graph in GraphRAG

O'Reilly on Data

presented the TRACE framework for measuring results, which showed how GraphRAG achieves an average performance improvement of up to 14.03%. Then connect the graph nodes and relations extracted from unstructured data sources, reusing the results of entity resolution to disambiguate terms within the domain context.

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5 tips for better business value from gen AI

CIO Business Intelligence

Set clear, measurable metrics around what you want to improve with generative AI, including the pain points and the opportunities, says Shaown Nandi, director of technology at AWS. Improving data quality and integrating new data sources to enrich customer and prospect data are vital for applying AI in marketing and sales.

Sales 143
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The Rise of Unstructured Data

Cloudera

Here we mostly focus on structured vs unstructured data. In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else.

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Two big things: They bring the messiness of the real world into your system through unstructured data. How will you measure success? So now we have a user persona, several scenarios, and a way to measure success. Business value : Align outputs with business metrics and optimize workflows to achieve measurable ROI.

Testing 168
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Measure Twice, Cut Once: How the Right Data Modeling Tool Drives Business Value

erwin

Additional challenges, such as increasing regulatory pressures – from the General Data Protection Regulation (GDPR) to the Health Insurance Privacy and Portability Act (HIPPA) – and growing stores of unstructured data also underscore the increasing importance of a data modeling tool.