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Unleashing the multimodal power of Amazon Bedrock Data Automation to transform unstructured data into actionable insights

AWS Machine Learning Blog

Traditionally, transforming raw data into actionable intelligence has demanded significant engineering effort. In a world whereaccording to Gartner over 80% of enterprise data is unstructured, enterprises need a better way to extract meaningful information to fuel innovation.

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Evaluate and improve performance of Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

There are two metrics used to evaluate retrieval: Context relevance Evaluates whether the retrieved information directly addresses the querys intent. It requires ground truth texts for comparison to assess recall and completeness of retrieved information. Implement metadata filtering , adding contextual layers to chunk retrieval.

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Asure’s approach to enhancing their call center experience using generative AI and Amazon Q in Quicksight

AWS Machine Learning Blog

Furthermore, by integrating a knowledge base containing organizational data, policies, and domain-specific information, the generative AI models can deliver more contextual, accurate, and relevant insights from the call transcripts. Architecture The following diagram illustrates the solution architecture. and Anthropics Claude Haiku 3.

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

Investment professionals face the mounting challenge of processing vast amounts of data to make timely, informed decisions. This challenge is particularly acute in credit markets, where the complexity of information and the need for quick, accurate insights directly impacts investment outcomes.

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Nielsen Sports sees 75% cost reduction in video analysis with Amazon SageMaker multi-model endpoints

AWS Machine Learning Blog

Through our understanding of people and their behaviors across all channels and platforms, we empower our clients with independent and actionable intelligence so they can connect and engage with their audiences—now and into the future. The information is delivered to the customer by a dashboard or analyst reports.

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Data Blending in Tableau

Pickl AI

Ultimately, Data Blending in Tableau fosters a deeper understanding of data dynamics and drives informed strategic actions. Data Blending is a technique used in data analysis to combine information from multiple datasets into a single unified view. What is Data Blending in tableau with an example?

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A Comprehensive Guide to the main components of Big Data

Pickl AI

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. For instance, Netflix uses diverse data types—from user viewing habits to movie metadata—to provide personalised recommendations.