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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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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. You ask the agent to Book a 5-day trip to Europe in January and we like warm weather.

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Can serverless fix fintech’s scaling problem?

CIO

With serverless components, there is no need to manage infrastructure, and the inbuilt tracing, logging, monitoring and debugging make it easy to run these workloads in production and maintain service levels. Financial services unique challenges However, it is important to understand that serverless architecture is not a silver bullet.

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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

Traditionally, building frontend and backend applications has required knowledge of web development frameworks and infrastructure management, which can be daunting for those with expertise primarily in data science and machine learning. Access to Amazon Bedrock foundation models is not granted by default.

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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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Building a Scalable ML Pipeline and API in AWS

Dzone - DevOps

With rapid progress in the fields of machine learning (ML) and artificial intelligence (AI), it is important to deploy the AI/ML model efficiently in production environments. The architecture downstream ensures scalability, cost efficiency, and real-time access to applications.

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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. API Gateway is serverless and hence automatically scales with traffic. You can use AWS services such as Application Load Balancer to implement this approach.