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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Software-as-a-service (SaaS) applications with tenant tiering SaaS applications are often architected to provide different pricing and experiences to a spectrum of customer profiles, referred to as tiers. The user prompt is then routed to the LLM associated with the task category of the reference prompt that has the closest match.

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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. This scalability allows for more frequent and comprehensive reviews.

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Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

As successful proof-of-concepts transition into production, organizations are increasingly in need of enterprise scalable solutions. For details on all the fields and providing configuration of various vector stores supported by Knowledge Bases for Amazon Bedrock, refer to AWS::Bedrock::KnowledgeBase.

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Integrating Key Vault Secrets with Azure Synapse Analytics

Apiumhub

Give each secret a clear name, as youll use these names to reference them in Synapse. Add a Linked Service to the pipeline that references the Key Vault. When setting up a linked service for these sources, reference the names of the secrets stored in Key Vault instead of hard-coding the credentials.

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Orchestrate generative AI workflows with Amazon Bedrock and AWS Step Functions

AWS Machine Learning - AI

Since Amazon Bedrock is serverless, you don’t have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. We're more than happy to provide further references upon request.

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Deploy DeepSeek-R1 Distilled Llama models in Amazon Bedrock

AWS Machine Learning - AI

Amazon Bedrock Custom Model Import enables the import and use of your customized models alongside existing FMs through a single serverless, unified API. This serverless approach eliminates the need for infrastructure management while providing enterprise-grade security and scalability.

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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.