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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 - AI

In this post, we show how to build a contextual text and image search engine for product recommendations using the Amazon Titan Multimodal Embeddings model , available in Amazon Bedrock , with Amazon OpenSearch Serverless. Store embeddings into the Amazon OpenSearch Serverless as the search engine. Review and prepare the dataset.

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Journey to Event Driven – Part 3: The Affinity Between Events, Streams and Serverless

Confluent

With serverless being all the rage, it brings with it a tidal change of innovation. or invest in a vendor-agnostic layer like the serverless framework ? or invest in a vendor-agnostic layer like the serverless framework ? What is more, as the world adopts the event-driven streaming architecture, how does it fit with serverless?

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Automate emails for task management using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails

AWS Machine Learning - AI

Amazon Bedrock offers a serverless experience so you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using AWS tools without having to manage infrastructure.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

For instance, consider an AI-driven legal document analysis system designed for businesses of varying sizes, offering two primary subscription tiers: Basic and Pro. This feature of Amazon Bedrock provides a single serverless endpoint for efficiently routing requests between different LLMs within the same model family.

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Accelerating insurance policy reviews with generative AI: Verisk’s Mozart companion

AWS Machine Learning - AI

Solution overview The policy documents reside in Amazon Simple Storage Service (Amazon S3) storage. During the solution design process, Verisk also considered using Amazon Bedrock Knowledge Bases because its purpose built for creating and storing embeddings within Amazon OpenSearch Serverless.

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High-performance computing on AWS

Xebia

Key features of AWS Batch Efficient Resource Management: AWS Batch automatically provisions the required resources, such as compute instances and storage, based on job requirements. This enables you to build end-to-end workflows that leverage the full range of AWS capabilities for data processing, storage, and analytics.

AWS 147
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Build an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and AWS CloudFormation

AWS Machine Learning - AI

Prerequisites To implement the solution provided in this post, you should have the following: An active AWS account and familiarity with FMs, Amazon Bedrock, and OpenSearch Serverless. He specializes in generative AI, machine learning, and system design. An S3 bucket where your documents are stored in a supported format (.txt,md,html,doc/docx,csv,xls/.xlsx,pdf).