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

AWS Machine Learning

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.

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

AWS Machine Learning

Furthermore, these notes are usually personal and not stored in a central location, which is a lost opportunity for businesses to learn what does and doesn’t work, as well as how to improve their sales, purchasing, and communication processes. It also supports audio files so you have flexibility around the type of call recordings you use.

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Enhance customer service efficiency with AI-powered summarization using Amazon Transcribe Call Analytics

AWS Machine Learning

To address these issues, we launched a generative artificial intelligence (AI) call summarization feature in Amazon Transcribe Call Analytics. Simply turn the feature on from the Amazon Transcribe console or using the start_call_analytics_job API. In this post, we show you how to use the new generative call summarization feature.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

Solution overview Our solution implements a verified semantic cache using the Amazon Bedrock Knowledge Bases Retrieve API to reduce hallucinations in LLM responses while simultaneously improving latency and reducing costs. The function checks the semantic cache (Amazon Bedrock Knowledge Bases) using the Retrieve API.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

In this post, we show how to use FMEval and Amazon SageMaker to programmatically evaluate LLMs. SageMaker is a data, analytics, and AI/ML platform, which we will use in conjunction with FMEval to streamline the evaluation process. We specifically focus on SageMaker with MLflow. This allows you to keep track of your ML experiments.

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2025 Guide to the Omnichannel Contact Center: How to Drive Success with the Right Software, Strategy, and Solutions

Calabrio

Workforce Management 2025 Guide to the Omnichannel Contact Center: How to Drive Success with the Right Software, Strategy, and Solutions Share Calling, email, texting, instant messaging, social mediathe communication channels available to us today can seem almost endless. Reporting and Analytics: Its all about visibility.

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From innovation to impact: How AWS and NVIDIA enable real-world generative AI success

AWS Machine Learning

The question is no longer whether to adopt generative AI, but how to move from promising pilots to production-ready systems that deliver real business value. Rahul has over twenty years of experience in technology and has co-founded two companies, one focused on analytics and the other on IP-geolocation.