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MaRDIFlow: Automating Metadata Abstraction for Enhanced Reproducibility in Computational Workflows

Marktechpost

Emerging tools like Jupyter notebooks and Code Ocean facilitate documentation and integration, while automated workflows aim to merge computer-based and laboratory computations. FMI’s container-based approach aids in replicating simulations but requires metadata for broader reproducibility and adaptation.

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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

Flipboard

Metadata can play a very important role in using data assets to make data driven decisions. Generating metadata for your data assets is often a time-consuming and manual task. This post shows you how to enrich your AWS Glue Data Catalog with dynamic metadata using foundation models (FMs) on Amazon Bedrock and your data documentation.

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How DPG Media uses Amazon Bedrock and Amazon Transcribe to enhance video metadata with AI-powered pipelines

AWS Machine Learning Blog

With a growing library of long-form video content, DPG Media recognizes the importance of efficiently managing and enhancing video metadata such as actor information, genre, summary of episodes, the mood of the video, and more. Video data analysis with AI wasn’t required for generating detailed, accurate, and high-quality metadata.

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Access control for vector stores using metadata filtering with Knowledge Bases for Amazon Bedrock

AWS Machine Learning Blog

With metadata filtering now available in Knowledge Bases for Amazon Bedrock, you can define and use metadata fields to filter the source data used for retrieving relevant context during RAG. Metadata filtering gives you more control over the RAG process for better results tailored to your specific use case needs.

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How Wealth Managers Can Build Trust Through the Power of Automation and AI

Unite.AI

The latest AI solutions now enable wealth managers to eradicate human error and secure integral daily processes, including knowledge work automation, which improve client experience and increases trust. Knowledge work automation, supported by metadata and AI technology, ensure wealth managers are accessing the correct data every time.

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Automate Amazon Bedrock batch inference: Building a scalable and efficient pipeline

AWS Machine Learning Blog

It stores information such as job ID, status, creation time, and other metadata. The following is a screenshot of the DynamoDB table where you can track the job status and other types of metadata related to the job. The DynamoDB table is crucial for tracking and managing the batch inference jobs throughout their lifecycle.

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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

Enterprises may want to add custom metadata like document types (W-2 forms or paystubs), various entity types such as names, organization, and address, in addition to the standard metadata like file type, date created, or size to extend the intelligent search while ingesting the documents.

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