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

AWS Machine Learning

We also dive deeper into access patterns, governance, responsible AI, observability, and common solution designs like Retrieval Augmented Generation. 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.

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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning

SageMaker Unified Studio setup SageMaker Unified Studio is a browser-based web application where you can use all your data and tools for analytics and AI. This will provision the backend infrastructure and services that the sales analytics application will rely on. You’ll use this file when setting up your function to query sales data.

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Shaping the future: OMRON’s data-driven journey with AWS

AWS Machine Learning

At the heart of this transformation is the OMRON Data & Analytics Platform (ODAP), an innovative initiative designed to revolutionize how the company harnesses its data assets. Data governance challenges Maintaining consistent data governance across different systems is crucial but complex.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

SageMaker is a data, analytics, and AI/ML platform, which we will use in conjunction with FMEval to streamline the evaluation process. It functions as a standalone HTTP server that provides various REST API endpoints for monitoring, recording, and visualizing experiment runs. We specifically focus on SageMaker with MLflow.

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Unlocking insights and enhancing customer service: Intact’s transformative AI journey with AWS

AWS Machine Learning

In this post, we demonstrate how the CQ solution used Amazon Transcribe and other AWS services to improve critical KPIs with AI-powered contact center call auditing and analytics. Additionally, Intact was impressed that Amazon Transcribe could adapt to various post-call analytics use cases across their organization.

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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. You can upload a call recording in Amazon S3 and start a Transcribe Call Analytics job.

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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning

These sessions, featuring Amazon Q Business , Amazon Q Developer , Amazon Q in QuickSight , and Amazon Q Connect , span the AI/ML, DevOps and Developer Productivity, Analytics, and Business Applications topics. Learn how Toyota utilizes analytics to detect emerging themes and unlock insights used by leaders across the enterprise.

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