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Introducing AWS MCP Servers for code assistants (Part 1)

AWS Machine Learning - AI

Were excited to announce the open source release of AWS MCP Servers for code assistants a suite of specialized Model Context Protocol (MCP) servers that bring Amazon Web Services (AWS) best practices directly to your development workflow. This post is the first in a series covering AWS MCP Servers.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. This allows teams to focus more on implementing improvements and optimizing AWS infrastructure. This systematic approach leads to more reliable and standardized evaluations.

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

AWS Machine Learning - AI

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can use AWS services such as Application Load Balancer to implement this approach. API Gateway also provides a WebSocket API. These components are illustrated in the following diagram.

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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning - AI

This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. Organizations typically can’t predict their call patterns, so the solution relies on AWS serverless services to scale during busy times.

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Harness the power of MCP servers with Amazon Bedrock Agents

AWS Machine Learning - AI

invoke(input_text=Convert 11am from NYC time to London time) We showcase an example of building an agent to understand your Amazon Web Service (AWS) spend by connecting to AWS Cost Explorer , Amazon CloudWatch , and Perplexity AI through MCP. This gives you an AI agent that can transform the way you manage your AWS spend.

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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning - AI

This solution uses decorators in your application code to capture and log metadata such as input prompts, output results, run time, and custom metadata, offering enhanced security, ease of use, flexibility, and integration with native AWS services.

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Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning - AI

Personalized care plans By using the LLMs knowledge base and analytical capabilities, healthcare professionals can develop tailored care plans aligned with the patients specific needs and medical history. An AWS account. If you dont have one, you can register for a new AWS account. Choose Test. Choose Test. Run the test event.