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Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

AWS Machine Learning Blog

With generative AI on AWS, you can reinvent your applications, create entirely new customer experiences, and improve overall productivity. You can use this post as a reference to build secure enterprise applications in the Generative AI domain using AWS services. Initialize the Python virtual environment. Go to the root folder.

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How Druva used Amazon Bedrock to address foundation model complexity when building Dru, Druva’s backup AI copilot

AWS Machine Learning Blog

Generate and run data transformation Python code. We tried different methods, including k-nearest neighbor (k-NN) search of vector embeddings, BM25 with synonyms , and a hybrid of both across fields including API routes, descriptions, and hypothetical questions. Generate and invoke private API calls.

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Build a contextual text and image search engine for product recommendations using Amazon Bedrock and Amazon OpenSearch Serverless

AWS Machine Learning Blog

We detail the steps to use an Amazon Titan Multimodal Embeddings model to encode images and text into embeddings, ingest embeddings into an OpenSearch Service index, and query the index using the OpenSearch Service k-nearest neighbors (k-NN) functionality. Virginia) and US West (Oregon) AWS Regions.

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Implement serverless semantic search of image and live video with Amazon Titan Multimodal Embeddings

AWS Machine Learning Blog

Kinesis Video Streams makes it straightforward to securely stream video from connected devices to AWS for analytics, machine learning (ML), playback, and other processing. Alternatively, you can use a serverless Lambda function to extract frames of a stored video file with the Python OpenCV library.

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Power recommendations and search using an IMDb knowledge graph – Part 3

AWS Machine Learning Blog

Many AWS media and entertainment customers license IMDb data through AWS Data Exchange to improve content discovery and increase customer engagement and retention. We downloaded the data from AWS Data Exchange and processed it in AWS Glue to generate KG files. Background. Solution overview. Launch solution resources.

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Build a crop segmentation machine learning model with Planet data and Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

In late 2023, Planet announced a partnership with AWS to make its geospatial data available through Amazon SageMaker. In this analysis, we use a K-nearest neighbors (KNN) model to conduct crop segmentation, and we compare these results with ground truth imagery on an agricultural region.

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Implement unified text and image search with a CLIP model using Amazon SageMaker and Amazon OpenSearch Service

AWS Machine Learning Blog

You can also use an AWS CloudFormation template by following the GitHub instructions to create a domain. By using an interface VPC endpoint (interface endpoint), the communication between your VPC and Studio is conducted entirely and securely within the AWS network. aws s3 cp $BUILD_ROOT/model.tar.gz $S3_PATH !bash

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