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Best practices for Amazon SageMaker HyperPod task governance

AWS Machine Learning

Administrators can use SageMaker HyperPod task governance to govern allocation of accelerated compute to teams and projects, and enforce policies that determine the priorities across different types of tasks. We also discuss common governance scenarios when administering and running generative AI development tasks.

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Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning

Principal implemented several measures to improve the security, governance, and performance of its conversational AI platform. Additional integrations with services like Amazon Data Firehose , AWS Glue , and Amazon Athena allowed for historical reporting, user activity analytics, and sentiment trends over time through Amazon QuickSight.

Chatbots 115
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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. Model monitoring – The model monitoring service allows tenants to evaluate model performance against predefined metrics.

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

AWS Machine Learning

They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels. This will provision the backend infrastructure and services that the sales analytics application will rely on.

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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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Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

AWS Machine Learning

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. However, ML governance plays a key role to make sure the data used in these models is accurate, secure, and reliable. For Select a data source , choose Athena.

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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. Evaluation algorithm Computes evaluation metrics to model outputs. Different algorithms have different metrics to be specified. We specifically focus on SageMaker with MLflow.