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AWS Lambda Benchmarking

Xebia

In this blog post, we examine the relative costs of different language runtimes on AWS Lambda. Many languages can be used with AWS Lambda today, so we focus on four interesting ones. Rust just came to AWS Lambda in November 2023 , so probably a lot of folks are wondering whether to try it out.

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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning - AI

Observability refers to the ability to understand the internal state and behavior of a system by analyzing its outputs, logs, and metrics. For a detailed breakdown of the features and implementation specifics, refer to the comprehensive documentation in the GitHub repository.

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Building Resilient Public Networking on AWS: Part 4

Xebia

In our example, our CloudWatch Alarms are fed by metrics generated by our ALB, but we could use any other metric that we thought could be more relevant. ClouDNS Documentation : Refer to the official ClouDNS documentation for detailed insights into their DNS hosting services and configurations.

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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. Shared components refer to the functionality and features shared by all tenants. Refer to Perform AI prompt-chaining with Amazon Bedrock for more details. Generative AI gateway Shared components lie in this part.

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Techniques and approaches for monitoring large language models on AWS

AWS Machine Learning - AI

In this post, we demonstrate a few metrics for online LLM monitoring and their respective architecture for scale using AWS services such as Amazon CloudWatch and AWS Lambda. Overview of solution The first thing to consider is that different metrics require different computation considerations. The function invokes the modules.

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CBRE and AWS perform natural language queries of structured data using Amazon Bedrock

AWS Machine Learning - AI

In this post, we describe how CBRE partnered with AWS Prototyping to develop a custom query environment allowing natural language query (NLQ) prompts by using Amazon Bedrock, AWS Lambda , Amazon Relational Database Service (Amazon RDS), and Amazon OpenSearch Service. A Lambda function with business logic invokes the primary Lambda function.

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Asure’s approach to enhancing their call center experience using generative AI and Amazon Q in Quicksight

AWS Machine Learning - AI

To learn more about the architectural components of the PCA solution, including file ingestion, insight extraction, storage and visualization, and web application components, refer to Post call analytics for your contact center with Amazon language AI services. In addition, traditional ML metrics were used for Yes/No answers.