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Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

AWS Machine Learning - AI

Traditional keyword-based search mechanisms are often insufficient for locating relevant documents efficiently, requiring extensive manual review to extract meaningful insights. This solution improves the findability and accessibility of archival records by automating metadata enrichment, document classification, and summarization.

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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning - AI

Exclusive to Amazon Bedrock, the Amazon Titan family of models incorporates 25 years of experience innovating with AI and machine learning at Amazon. Store embeddings : Ingest the generated embeddings into an OpenSearch Serverless vector index, which serves as the vector database for the solution.

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning - AI

That’s where the new Amazon EMR Serverless application integration in Amazon SageMaker Studio can help. In this post, we demonstrate how to leverage the new EMR Serverless integration with SageMaker Studio to streamline your data processing and machine learning workflows.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices. An interactive chat interface allows deeper exploration of both the original document and generated content.

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Amazon Bedrock Flows is now generally available with enhanced safety and traceability

AWS Machine Learning - AI

Reduced time and effort in testing and deploying AI workflows with SDK APIs and serverless infrastructure. We can also quickly integrate flows with our applications using the SDK APIs for serverless flow execution — without wasting time in deployment and infrastructure management.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

API Gateway is serverless and hence automatically scales with traffic. The advantage of using Application Load Balancer is that it can seamlessly route the request to virtually any managed, serverless or self-hosted component and can also scale well. It’s serverless so you don’t have to manage the infrastructure.

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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning - AI

Cost optimization – This solution uses serverless technologies, making it cost-effective for the observability infrastructure. For a detailed breakdown of the features and implementation specifics, refer to the comprehensive documentation in the GitHub repository. However, some components may incur additional usage-based costs.