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Extend large language models powered by Amazon SageMaker AI using Model Context Protocol

AWS Machine Learning - AI

For MCP implementation, you need a scalable infrastructure to host these servers and an infrastructure to host the large language model (LLM), which will perform actions with the tools implemented by the MCP server. We will deep dive into the MCP architecture later in this post.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements.

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Automate Amazon Bedrock batch inference: Building a scalable and efficient pipeline

AWS Machine Learning - AI

To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. We walk you through our solution, detailing the core logic of the Lambda functions. Amazon S3 invokes the {stack_name}-create-batch-queue-{AWS-Region} Lambda function.

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Techniques and approaches for monitoring large language models on AWS

AWS Machine Learning - AI

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP), improving tasks such as language translation, text summarization, and sentiment analysis. Monitoring the performance and behavior of LLMs is a critical task for ensuring their safety and effectiveness.

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Revolutionize trip planning with Amazon Bedrock and Amazon Location Service

AWS Machine Learning - AI

This innovative service goes beyond traditional trip planning methods, offering real-time interaction through a chat-based interface and maintaining scalability, reliability, and data security through AWS native services. An agent uses the power of an LLM to determine which function to execute, and output the result based on the prompt guide.

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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

National Laboratory has implemented an AI-driven document processing platform that integrates named entity recognition (NER) and large language models (LLMs) on Amazon SageMaker AI. In this post, we discuss how you can build an AI-powered document processing platform with open source NER and LLMs on SageMaker.

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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

The solution integrates large language models (LLMs) with your organization’s data and provides an intelligent chat assistant that understands conversation context and provides relevant, interactive responses directly within the Google Chat interface. Which LLM you want to use in Amazon Bedrock for text generation.