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Building Resilient Public Networking on AWS: Part 4

Xebia

Region Evacuation with DNS Approach: Our third post discussed deploying web server infrastructure across multiple regions and reviewed the DNS regional evacuation approach using AWS Route 53. But to keep this example as simple as possible, we will use a built-in feature of AWS Global Accelerator that routes traffic to the healthy endpoints.

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Introducing AWS MCP Servers for code assistants (Part 1)

AWS Machine Learning - AI

Were excited to announce the open source release of AWS MCP Servers for code assistants a suite of specialized Model Context Protocol (MCP) servers that bring Amazon Web Services (AWS) best practices directly to your development workflow. Developers need code assistants that understand the nuances of AWS services and best practices.

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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning - AI

Use case overview The organization in this scenario has noticed that during customer calls, some actions often get skipped due to the complexity of the discussions, and that there might be potential to centralize customer data to better understand how to improve customer interactions in the long run.

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

AWS Machine Learning - AI

Although the principles discussed are applicable across various industries, we use an automotive parts retailer as our primary example throughout this post. The Lambda function runs the database query against the appropriate OpenSearch Service indexes, searching for exact matches or using fuzzy matching for partial information.

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Harness the power of MCP servers with Amazon Bedrock Agents

AWS Machine Learning - AI

However, in the past, connecting these agents to diverse enterprise systems has created development bottlenecks, with each integration requiring custom code and ongoing maintenancea standardization challenge that slows the delivery of contextual AI assistance across an organizations digital ecosystem.

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

For example, if ground truth is generated by LLMs before the involvement of SMEs, SMEs will still be needed to identify which questions are fundamental to the business and then align the ground truth with business value as part of a human-in-the-loop process. For our example, we work with Anthropics Claude LLM on Amazon Bedrock.