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

AWS Machine Learning - AI

Shared components refer to the functionality and features shared by all tenants. 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.

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Pixtral Large is now available in Amazon Bedrock

AWS Machine Learning - AI

AWS is the first major cloud provider to deliver Pixtral Large as a fully managed, serverless model. For more information on generating JSON using the Converse API, refer to Generating JSON with the Amazon Bedrock Converse API. In this post, we discuss the features of Pixtral Large and its possible use cases.

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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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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.

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

AWS Machine Learning - AI

If you don’t have an AWS account, refer to How do I create and activate a new Amazon Web Services account? If you don’t have an existing knowledge base, refer to Create an Amazon Bedrock knowledge base. Performance optimization The serverless architecture used in this post provides a scalable solution out of the box.