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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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Streamlining Workflows with Feature Branches and Logical Stacks

Xebia

However, proper strategies can make managing resources, dependencies, and environments challenging. This blog explores how to optimize feature branch workflows, maintain encapsulated logical stacks, and apply best practices like resource naming to improve clarity, scalability, and cost-effectiveness.

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Track, allocate, and manage your generative AI cost and usage with Amazon Bedrock

AWS Machine Learning - AI

Without a scalable approach to controlling costs, organizations risk unbudgeted usage and cost overruns. This scalable, programmatic approach eliminates inefficient manual processes, reduces the risk of excess spending, and ensures that critical applications receive priority.

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

AWS Machine Learning - AI

Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message. After it’s authenticated, the request is forwarded to another Lambda function that contains our core application logic. This request contains the user’s message and relevant metadata.

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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning - AI

The Lambda function runs the database query against the appropriate OpenSearch Service indexes, searching for exact matches or using fuzzy matching for partial information. The Lambda function processes the OpenSearch Service results and formats them for the Amazon Bedrock agent.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Such a virtual assistant should support users across various business functions, such as finance, legal, human resources, and operations. This hybrid approach combines the scalability and flexibility of semantic search with the precision and context-awareness of classifier LLMs. However, it also presents some trade-offs.

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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. Depending on the use case and data isolation requirements, tenants can have a pooled knowledge base or a siloed one and implement item-level isolation or resource level isolation for the data respectively.