Remove Automation Remove Machine Learning Remove Metadata
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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

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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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Multi-tenancy in RAG applications in a single Amazon Bedrock knowledge base with metadata filtering

AWS Machine Learning Blog

One of these strategies is using Amazon Simple Storage Service (Amazon S3) folder structures and Amazon Bedrock Knowledge Bases metadata filtering to enable efficient data segmentation within a single knowledge base. The S3 bucket, containing customer data and metadata, is configured as a knowledge base data source.

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

AWS Machine Learning Blog

It often requires managing multiple machine learning (ML) models, designing complex workflows, and integrating diverse data sources into production-ready formats. With Amazon Bedrock Data Automation, enterprises can accelerate AI adoption and develop solutions that are secure, scalable, and responsible.

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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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Process formulas and charts with Anthropic’s Claude on Amazon Bedrock

AWS Machine Learning Blog

However, by using Anthropics Claude on Amazon Bedrock , researchers and engineers can now automate the indexing and tagging of these technical documents. This enables the efficient processing of content, including scientific formulas and data visualizations, and the population of Amazon Bedrock Knowledge Bases with appropriate metadata.

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Streamline AWS resource troubleshooting with Amazon Bedrock Agents and AWS Support Automation Workflows

AWS Machine Learning Blog

Fortunately, AWS provides a powerful tool called AWS Support Automation Workflows , which is a collection of curated AWS Systems Manager self-service automation runbooks. These runbooks are created by AWS Support Engineering with best practices learned from solving customer issues. The agent uses Anthropics Claude 3.5