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From “Secondary Storage” To Just “Storage”: A Tale of Lambdas, LZ4, and Garbage Collection

Honeycomb

When we introduced Secondary Storage two years ago, it was a deliberate compromise between economy and performance. Compared to Honeycomb’s primary NVMe storage attached to dedicated servers, secondary storage let customers keep more data for less money. Enter AWS Lambda. Today things look very different.

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Spend Smarter, Not More: A Guide to AWS Storage Cost Optimization

Xebia

Introduction With an ever-expanding digital universe, data storage has become a crucial aspect of every organization’s IT strategy. S3 Storage Undoubtedly, anyone who uses AWS will inevitably encounter S3, one of the platform’s most popular storage services. Storage Class Designed For Retrieval Change Min.

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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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Improve DynamoDB query performance using summary data

Xebia

Given this query, the getTotalByOwner function in transaction/lambda/database.ts We can therefore save a bit of storage space by adding a summary every X records (10 in the code example). The post Improve DynamoDB query performance using summary data appeared first on Xebia.

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Techniques and approaches for monitoring large language models on AWS

AWS Machine Learning - AI

However, as these models continue to grow in size and complexity, monitoring their performance and behavior has become increasingly challenging. Monitoring the performance and behavior of LLMs is a critical task for ensuring their safety and effectiveness. The file saved on Amazon S3 creates an event that triggers a Lambda function.

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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. The code runs in a Lambda function. Implement your business logic in this file.

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

AWS Machine Learning - AI

The agents also automatically call APIs to perform actions and access knowledge bases to provide additional information. The workflow includes the following steps: Documents (owner manuals) are uploaded to an Amazon Simple Storage Service (Amazon S3) bucket. The following diagram illustrates how it works.

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