Remove 2012 Remove Metadata Remove Python
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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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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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Architecture to AWS CloudFormation code using Anthropic’s Claude 3 on Amazon Bedrock

AWS Machine Learning Blog

Ref S3BucketName, '/*']] - PolicyName: SNSPublish PolicyDocument: Version: '2012-10-17' Statement: - Effect: Allow Action: - 'sns:Publish' Resource: !Ref Second, we want to add metadata to the CloudFormation template. Join ['', ['arn:aws:s3:::', !Ref GetAtt ProcessingLambda.Arn Action: 'lambda:InvokeFunction' Principal: s3.amazonaws.com

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Manage Amazon SageMaker JumpStart foundation model access with private hubs

AWS Machine Learning Blog

Solution overview Starting today, with SageMaker JumpStart and its private hub feature, administrators can create repositories for a subset of models tailored to different teams, use cases, or license requirements using the Amazon SageMaker Python SDK. For a list of filters you can apply, refer to SageMaker Python SDK.

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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning Blog

After modeling, detected services of each architecture diagram image and its metadata, like URL origin and image title, are indexed for future search purposes and stored in Amazon DynamoDB , a fully managed, serverless, key-value NoSQL database designed to run high-performance applications. join(", "), }; }).catch((error) join(", "), }; }).catch((error)

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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning Blog

MLflow has integrated the feature that enables request signing using AWS credentials into the upstream repository for its Python SDK, improving the integration with SageMaker. The changes to the MLflow Python SDK are available for everyone since MLflow version 1.30.0. At this point, the MLflow SDK only needs AWS credentials.

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Optimized Deep Learning Pipelines: A Deep Dive into TFRecords and Protobufs (Part 2)

Heartbeat

If you’ve only been programming in Python land your whole life, and have no clue what I mean when I say map, you can think of it as no different than a Python dictionary. For example, “Features” would have to become: Again, if you’ve only ever programmed in Python, or something of the sort, this might seem strange to you.