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How Government (and Any Business) Can Transform Customer Experience with Gabriele Masili

ShepHyken

Trust is essential when dealing with government services. When people trust their government, they have better experiences. When people feel understood and valued, their trust in the service provider, whether the government or private companies, grows. Plus, G shares how staffing issues affect government service delivery.

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. However, as data volumes and complexity continue to grow, effective data governance becomes a critical challenge.

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Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning

For a multi-account environment, you can track costs at an AWS account level to associate expenses. A combination of an AWS account and tags provides the best results. Tagging is an effective scaling mechanism for implementing cloud management and governance strategies.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Human oversight : Including human involvement in AI decision-making processes.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.

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Enable Amazon Bedrock cross-Region inference in multi-account environments

AWS Machine Learning

The customers AWS accounts that are allowed to use Amazon Bedrock are under an Organizational Unit (OU) called Sandbox. We want to enable the accounts under the Sandbox OU to use Anthropics Claude 3.5 Use case For our sample use case, we use Regions us-east-1 and us-west-2. Sonnet v2 model using cross-Region inference. MULTISERVICE.PV.1

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Secure distributed logging in scalable multi-account deployments using Amazon Bedrock and LangChain

AWS Machine Learning

Some companies go to great lengths to maintain confidentiality, sometimes adopting multi-account architectures, where each customer has their data in a separate AWS account. In this post, we present a solution for securing distributed logging multi-account deployments using Amazon Bedrock and LangChain.